R Notebook: estimation of impact of arguments on gas costs; for Gas Cost Estimator; for a single given env
In this script we conduct the estimation for the
measure_arguments approach.
PROGRAMS=pg_arguments_full5_c200_opc15x2 SAMPLESIZE=50 NSAMPLES=1`.
Expected a result file nethermind_pg_arguments_full5_c200_opc15x2_a.csv.
# the programs file is too large to be placed in github
programs = read.csv(paste("../../local/", program_set_codename, ".csv", sep=""))
results = load_data_set(env, program_set_codename, measurement_codename)
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "measure_total_time_ns", "measure_total_timer_time_ns", "env")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$measure_total_time_ns != 0), ]
all_envs = c(env)measurements = sqldf("SELECT opcode, op_count, arg0, arg1, arg2, sample_id, run_id, measure_total_time_ns, env, results.program_id
FROM results
INNER JOIN
programs ON(results.program_id = programs.program_id)
")
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
head(measurements)## opcode op_count arg0 arg1 arg2 sample_id run_id measure_total_time_ns
## 1 ADD 0 25 27 NA 0 1 11809.71
## 2 ADD 15 25 27 NA 0 1 11341.53
## 3 ADD 30 25 27 NA 0 1 12562.83
## 4 ADD 0 14 9 NA 0 1 11796.06
## 5 ADD 15 14 9 NA 0 1 12456.87
## 6 ADD 30 14 9 NA 0 1 12173.68
## env program_id
## 1 nethermind ADD_0
## 2 nethermind ADD_1
## 3 nethermind ADD_2
## 4 nethermind ADD_3
## 5 nethermind ADD_4
## 6 nethermind ADD_5Remove outliers if needed.
# Extracts all OPCODEs from the `programs` data frame of the given arity (args taken off the stack).
extract_opcodes <- function(arity) {
if (!missing(arity)) {
if (arity == 0) {
programs = programs[which(is.na(programs$arg0) & is.na(programs$arg1) & is.na(programs$arg2)), ]
}
if (arity == 1) {
programs = programs[which(!is.na(programs$arg0) & is.na(programs$arg1) & is.na(programs$arg2)), ]
}
if (arity == 2) {
programs = programs[which(!is.na(programs$arg1) & is.na(programs$arg2)), ]
}
if (arity == 3) {
programs = programs[which(!is.na(programs$arg2)), ]
}
}
unique(programs$opcode)
}if ( (!removed_outliers) && (!removed_outliers_2)) {
boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'all'))
}if (removed_outliers) {
par(mfrow=c(length(all_envs)*2, 1))
# before
boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'all'))
measurements = remove_outliers(measurements, 'measure_total_time_ns', FALSE)
# after
boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'no_outliers'))
}all_opcodes = extract_opcodes()
nullary_opcodes = extract_opcodes(0)
unary_opcodes = extract_opcodes(1)
binary_opcodes = extract_opcodes(2)
ternary_opcodes = extract_opcodes(3)
div_opcodes = c('DIV', 'MOD', 'SDIV', 'SMOD')measurements$expensive = NA
measurements[which(measurements$opcode %in% div_opcodes), ]$expensive =
measurements[which(measurements$opcode %in% div_opcodes), ]$arg0 >
measurements[which(measurements$opcode %in% div_opcodes), ]$arg1
# remember that argX is the byte-size of the argument in these measurements
measurements[which(measurements$opcode == 'ADDMOD'), ]$expensive =
8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg0 +
8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg1 >
8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg2
measurements[which(measurements$opcode == 'MULMOD'), ]$expensive =
measurements[which(measurements$opcode == 'MULMOD'), ]$arg0 +
measurements[which(measurements$opcode == 'MULMOD'), ]$arg1 >
measurements[which(measurements$opcode == 'MULMOD'), ]$arg2if (removed_outliers_2) {
measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', all_envs)
}
Detailed view
This is massive and detailed overview on the impact of arguments.
Because of the number of charts, only op count = 30 is
eligible. Feel free to change it, but that should not be anyhow more
informative. The visualizations do not guarantee that all dependencies
are clearly seen. Especially for binary and ternary opcodes where
impacts of arg0, arg1 and arg2 are mixed. But if a dependency is
graphically noticeable that you should expect also statistical
dependency.
for (env in all_envs) {
for (opcode in unary_opcodes) {
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg0', 'opcount 0'))
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg0', 'opcount 15'))
plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg0', 'opcount 30'))
}
for (opcode in binary_opcodes) {
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg0', 'opcount 0'))
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg0', 'opcount 15'))
plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg0', 'opcount 30'))
# plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg1', 'opcount 0'))
# plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg1', 'opcount 15'))
plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg1', 'opcount 30'))
}
for (opcode in ternary_opcodes) {
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg0', 'opcount 0'))
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg0', 'opcount 15'))
plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg0', 'opcount 30'))
# plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg1', 'opcount 0'))
# plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg1', 'opcount 15'))
plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg1', 'opcount 30'))
# plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg2', 'opcount 0'))
# plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg2', 'opcount 15'))
plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg2', 'opcount 30'))
}
}
Models
Notes: 1. Outliers need to be removed if detected 2. The
argX:op_count interactions measure the impact on the OPCODE
3. The argX are just auxiliary variables added to exclude
the effect of cheaper/more expensive PUSHes. We only want to extract the
effect of the argument on the measured OPCODE repeated
op_count times.
# Every `arg` coefficient represents the impact of the argument's byte size growing by 1.
# We treat as impactful the arguments where p-value is effectively zero. The previous approach was:
# Treat as impactful the arguments, where:
# 1. The estimate is significant with confidence 0.001
# 2. The increase of arg's byte size by 1 will increase the cost by more than 1%
# but it turned out to be much less stable in practice.
p_value_thresh = 1e-30
# p_value_thresh = 0.001
impact_ratio = 0.00
# impact_ratio = 0.01
arg_lm <- function(df, opcode, env, formula) {
data = df[which(df$opcode==opcode & df$env==env), ]
lm(formula, data=data)
}
# Adds the results from the estimated `model` to the `results_df` data frame.
# You need to provide the corresponding `opcode`, `env` and `arity`.
# `results_df` is assumed to have the columns as the `first_pass` data frame has (see below)
add_arg_results <- function(model, opcode, env, results_df, arity) {
stopifnot(arity > 0)
all_coefficients = summary(model)$coefficients
arg_coefficients = all_coefficients[!(row.names(all_coefficients) %in% c("op_count", "(Intercept)", "arg0", "arg1", "arg2")),]
pure_op_count_coeff = all_coefficients["op_count", 1]
# will be filled if any is impacting
args_ns = c(NA, NA, NA)
# will be always if arg present
args_ns_raw = c(NA, NA, NA)
args_ns_p = c(NA, NA, NA)
if (arity == 1) {
# there's only one arg coefficient here, silly R forces us to take a special case path...
has_significant = arg_coefficients[4] < p_value_thresh
if (has_significant) {
coefficient_impact = abs(arg_coefficients[1])
has_impacting = has_significant & coefficient_impact > pure_op_count_coeff * impact_ratio
} else {
has_impacting = FALSE
}
if (has_impacting) {
args_ns[1] = arg_coefficients[1]
}
args_ns_raw[1] = arg_coefficients[1]
args_ns_p[1] = arg_coefficients[4]
} else {
significant = arg_coefficients[, 4] < p_value_thresh
has_significant = length(which(significant)) > 0
coefficient_impact = abs(arg_coefficients[, 1])
can_impact = significant & coefficient_impact > pure_op_count_coeff * impact_ratio
has_impacting = length(which(can_impact)) > 0
args_ns[which(can_impact)] = arg_coefficients[which(can_impact), 1]
args_ns_raw[1:arity] = arg_coefficients[1:arity, 1]
args_ns_p[1:arity] = arg_coefficients[1:arity, 4]
}
# NAs for the "expensive" arg columns. See above for the columns layout
results_df[nrow(results_df) + 1, ] = c(opcode, env, has_significant, has_impacting, pure_op_count_coeff, args_ns, NA, args_ns_raw, NA, args_ns_p, NA)
return(results_df)
}
# Adds the results from the estimated `model` to the `results_df` data frame, where the model is
# specifically the one gauged towards the "division" OPCODEs like `DIV`.
# See also `add_arg_results`
add_arg_expensive_results <- function(model, opcode, env, results_df, arity) {
stopifnot(arity > 0)
all_coefficients = summary(model)$coefficients
pure_op_count_coeff = all_coefficients["op_count", 1]
expensive = NA
# there's only one arg coefficient here, silly R forces us to take a special case path...
has_significant = all_coefficients['op_count:expensiveTRUE', 4] < p_value_thresh
if (has_significant) {
coefficient_impact = abs(all_coefficients['op_count:expensiveTRUE', 1])
has_impacting = has_significant & coefficient_impact > pure_op_count_coeff * impact_ratio
} else {
has_impacting = FALSE
}
if (has_impacting) {
expensive = all_coefficients['op_count:expensiveTRUE', 1]
}
expensive_raw = all_coefficients['op_count:expensiveTRUE', 1]
expensive_p = all_coefficients['op_count:expensiveTRUE', 4]
results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns'] = expensive
results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns_raw'] = expensive_raw
results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns_p'] = expensive_p
return(results_df)
}
# Goes through all the families of OPCODEs and fits and displays their respective `measure_arguments`
# models.
# Results are gathered in a common `results_df` data frame.
analyze_for_env <- function(df, results_df, env) {
for (opcode in unary_opcodes) {
model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg0:op_count)
print(c(opcode, env))
print(summary(model))
results_df = add_arg_results(model, opcode, env, results_df, 1)
}
for (opcode in binary_opcodes) {
model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg0:op_count + arg1:op_count)
print(c(opcode, env))
print(summary(model))
results_df = add_arg_results(model, opcode, env, results_df, 2)
}
for (opcode in ternary_opcodes) {
model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg2 + arg0:op_count + arg1:op_count + arg2:op_count)
print(c(opcode, env))
print(summary(model))
results_df = add_arg_results(model, opcode, env, results_df, 3)
}
for (opcode in div_opcodes) {
model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + expensive:op_count)
print(c(opcode, env))
print(summary(model))
results_df = add_arg_expensive_results(model, opcode, env, results_df, 2)
}
for (opcode in c('ADDMOD', 'MULMOD')) {
model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg2 + expensive:op_count)
print(c(opcode, env))
print(summary(model))
results_df = add_arg_expensive_results(model, opcode, env, results_df, 3)
}
return(results_df)
}#model = arg_lm(measurements, 'EXP', env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg0/op_count + arg1/op_count)
model = lm(measure_total_time_ns ~ op_count + arg0 + arg1 + arg0:op_count + arg1:op_count, data=measurements[which(measurements$opcode=='EXP' & measurements$env==env), ])
model##
## Call:
## lm(formula = measure_total_time_ns ~ op_count + arg0 + arg1 +
## arg0:op_count + arg1:op_count, data = measurements[which(measurements$opcode ==
## "EXP" & measurements$env == env), ])
##
## Coefficients:
## (Intercept) op_count arg0 arg1 op_count:arg0
## 8482.6999 870.2124 161.4904 195.9630 -0.7686
## op_count:arg1
## 274.3254plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == 'EXP' & measurements$op_count == 0), ], pch=5, col='blue')
title(main = paste(env, 'EXP', 'arg1', 'opcount 0'))
model = lm(measure_total_time_ns ~ arg0 + arg1, data=measurements[which(measurements$opcode=='EXP' & measurements$env==env & measurements$op_count==0), ])
model##
## Call:
## lm(formula = measure_total_time_ns ~ arg0 + arg1, data = measurements[which(measurements$opcode ==
## "EXP" & measurements$env == env & measurements$op_count ==
## 0), ])
##
## Coefficients:
## (Intercept) arg0 arg1
## 11331.152 -3.873 -1.112plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == 'EXP' & measurements$op_count == 15), ], pch=5, col='blue')
title(main = paste(env, 'EXP', 'arg1', 'opcount 15'))
model = lm(measure_total_time_ns ~ arg0 + arg1, data=measurements[which(measurements$opcode=='EXP' & measurements$env==env & measurements$op_count==15), ])
model##
## Call:
## lm(formula = measure_total_time_ns ~ arg0 + arg1, data = measurements[which(measurements$opcode ==
## "EXP" & measurements$env == env & measurements$op_count ==
## 15), ])
##
## Coefficients:
## (Intercept) arg0 arg1
## 14819.9 502.5 4789.4plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == 'EXP' & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, 'EXP', 'arg1', 'opcount 30'))
model = lm(measure_total_time_ns ~ arg0 + arg1, data=measurements[which(measurements$opcode=='EXP' & measurements$env==env & measurements$op_count==30), ])
model##
## Call:
## lm(formula = measure_total_time_ns ~ arg0 + arg1, data = measurements[which(measurements$opcode ==
## "EXP" & measurements$env == env & measurements$op_count ==
## 30), ])
##
## Coefficients:
## (Intercept) arg0 arg1
## 37387.13 -25.29 8234.54max_m = max(measurements[which(measurements$env == env & measurements$opcode == 'EXP'), 'measure_total_time_ns'])
plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == 'EXP' & measurements$op_count == 0), ], pch=5, col='red', ylim=c(0,max_m * 1.1))
points(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == 'EXP' & measurements$op_count == 15), ], pch=5, col='green', ylim=c(0,max_m * 1.1))
points(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == 'EXP' & measurements$op_count == 30), ], pch=5, col='blue', ylim=c(0,max_m * 1.1))
title(main = paste(env, 'EXP', 'arg1'))
This is the so-called “first-pass” at the estimation procedure, where we
estimated all possible argument impact variables for all OPCODEs. We
gather all the results in the first_pass table, inspect
this to see where the arguments turned out to be significantly impacting
the computation cost.
first_pass = data.frame(matrix(ncol = 17, nrow = 0))
colnames(first_pass) <- c('opcode', 'env', 'has_significant', 'has_impacting', 'estimate_marginal_ns',
'arg0_ns', 'arg1_ns', 'arg2_ns', 'expensive_ns',
'arg0_ns_raw', 'arg1_ns_raw', 'arg2_ns_raw', 'expensive_ns_raw',
'arg0_ns_p', 'arg1_ns_p', 'arg2_ns_p', 'expensive_ns_p')
first_pass = analyze_for_env(measurements, first_pass, env)## [1] "ISZERO" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1185.12 -325.36 -78.94 339.50 1245.30
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11250.7414 56.5172 199.067 < 0.0000000000000002 ***
## op_count 18.3983 2.8832 6.381 0.000000000357 ***
## arg0 -1.6243 3.1411 -0.517 0.605
## op_count:arg0 0.1181 0.1607 0.735 0.463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 428.2 on 587 degrees of freedom
## Multiple R-squared: 0.2521, Adjusted R-squared: 0.2483
## F-statistic: 65.97 on 3 and 587 DF, p-value: < 0.00000000000000022
##
## [1] "NOT" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1062.2 -337.7 -105.2 366.0 1528.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11241.96032 62.21202 180.704 <0.0000000000000002 ***
## op_count 31.34877 3.20970 9.767 <0.0000000000000002 ***
## arg0 1.05525 3.20289 0.329 0.742
## op_count:arg0 0.05906 0.16566 0.356 0.722
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 449.6 on 584 degrees of freedom
## Multiple R-squared: 0.4393, Adjusted R-squared: 0.4365
## F-statistic: 152.5 on 3 and 584 DF, p-value: < 0.00000000000000022
##
## [1] "CALLDATALOAD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -975.9 -304.6 -105.3 220.4 1228.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13166.02423542 61.76352733 213.168 <0.0000000000000002 ***
## op_count 27.25275273 3.20699014 8.498 <0.0000000000000002 ***
## arg0 0.00004723 0.00617179 0.008 0.994
## op_count:arg0 0.00010752 0.00031942 0.337 0.737
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 455.1 on 581 degrees of freedom
## Multiple R-squared: 0.3673, Adjusted R-squared: 0.3641
## F-statistic: 112.4 on 3 and 581 DF, p-value: < 0.00000000000000022
##
## [1] "POP" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -695.05 -271.56 -41.15 249.35 1099.26
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10073.7958 45.3564 222.103 < 0.0000000000000002 ***
## op_count 8.3638 2.3440 3.568 0.000389 ***
## arg0 0.5889 2.3819 0.247 0.804820
## op_count:arg0 -0.1105 0.1228 -0.899 0.368813
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 343.4 on 590 degrees of freedom
## Multiple R-squared: 0.05359, Adjusted R-squared: 0.04878
## F-statistic: 11.14 on 3 and 590 DF, p-value: 0.0000004052
##
## [1] "MLOAD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1088.13 -338.24 -93.68 204.30 1908.72
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13043.9951626 72.1963916 180.674 <0.0000000000000002 ***
## op_count 73.0671208 3.6900001 19.801 <0.0000000000000002 ***
## arg0 0.0007421 0.0070816 0.105 0.917
## op_count:arg0 -0.0004458 0.0003628 -1.229 0.220
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 508.7 on 567 degrees of freedom
## Multiple R-squared: 0.7382, Adjusted R-squared: 0.7368
## F-statistic: 533 on 3 and 567 DF, p-value: < 0.00000000000000022
##
## [1] "JUMPI" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2242.3 -1006.2 -325.4 848.9 3848.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12046.5226 168.6941 71.410 <0.0000000000000002 ***
## op_count 174.8372 8.6989 20.099 <0.0000000000000002 ***
## arg0 3.2191 9.1250 0.353 0.724
## op_count:arg0 -0.4237 0.4700 -0.902 0.368
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1334 on 589 degrees of freedom
## Multiple R-squared: 0.7061, Adjusted R-squared: 0.7046
## F-statistic: 471.7 on 3 and 589 DF, p-value: < 0.00000000000000022
##
## [1] "DUP1" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -880.06 -292.44 -73.72 288.48 1207.54
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11310.0280 53.0715 213.109 < 0.0000000000000002 ***
## op_count 7.7732 2.7533 2.823 0.00492 **
## arg0 -3.1260 2.7489 -1.137 0.25593
## op_count:arg0 0.1615 0.1424 1.134 0.25723
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 389.7 on 583 degrees of freedom
## Multiple R-squared: 0.1013, Adjusted R-squared: 0.09663
## F-statistic: 21.89 on 3 and 583 DF, p-value: 0.0000000000001902
##
## [1] "DUP2" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -973.40 -308.76 -55.39 313.34 1338.03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11272.21787 50.96969 221.155 < 0.0000000000000002 ***
## op_count 10.22612 2.63486 3.881 0.000116 ***
## arg0 0.24889 2.70779 0.092 0.926797
## op_count:arg0 0.03859 0.13985 0.276 0.782711
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 403.7 on 592 degrees of freedom
## Multiple R-squared: 0.09898, Adjusted R-squared: 0.09441
## F-statistic: 21.68 on 3 and 592 DF, p-value: 0.000000000000248
##
## [1] "DUP3" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1124.92 -323.18 -48.73 334.05 1217.30
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11349.00948 53.41737 212.459 < 0.0000000000000002 ***
## op_count 8.78791 2.75797 3.186 0.00152 **
## arg0 -4.06431 2.90259 -1.400 0.16197
## op_count:arg0 0.04351 0.15103 0.288 0.77338
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 398.8 on 586 degrees of freedom
## Multiple R-squared: 0.08423, Adjusted R-squared: 0.07954
## F-statistic: 17.97 on 3 and 586 DF, p-value: 0.00000000003634
##
## [1] "DUP4" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1005.34 -298.38 -34.55 311.57 1306.53
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11283.8593 50.9879 221.305 < 0.0000000000000002 ***
## op_count 7.3486 2.6286 2.796 0.00535 **
## arg0 -1.8147 2.6914 -0.674 0.50042
## op_count:arg0 0.2262 0.1391 1.626 0.10441
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 388.1 on 588 degrees of freedom
## Multiple R-squared: 0.1143, Adjusted R-squared: 0.1097
## F-statistic: 25.28 on 3 and 588 DF, p-value: 0.000000000000002142
##
## [1] "DUP5" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -989.86 -311.99 -57.19 326.51 1357.53
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11303.81551 52.68257 214.565 < 0.0000000000000002 ***
## op_count 9.37518 2.73141 3.432 0.000641 ***
## arg0 -1.20759 2.74764 -0.440 0.660461
## op_count:arg0 0.06337 0.14213 0.446 0.655856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 405.5 on 584 degrees of freedom
## Multiple R-squared: 0.09091, Adjusted R-squared: 0.08624
## F-statistic: 19.47 on 3 and 584 DF, p-value: 0.00000000000485
##
## [1] "DUP6" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -914.38 -366.73 -50.13 336.84 1470.53
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11168.0328 61.7393 180.890 < 0.0000000000000002 ***
## op_count 13.4579 3.1898 4.219 0.0000284 ***
## arg0 5.8747 3.1998 1.836 0.0669 .
## op_count:arg0 -0.1347 0.1656 -0.814 0.4162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 438.5 on 591 degrees of freedom
## Multiple R-squared: 0.09494, Adjusted R-squared: 0.09035
## F-statistic: 20.67 on 3 and 591 DF, p-value: 0.0000000000009585
##
## [1] "DUP7" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1180.25 -321.83 -29.03 302.21 1354.04
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11272.2325 57.9754 194.431 < 0.0000000000000002 ***
## op_count 14.0243 2.9902 4.690 0.0000034 ***
## arg0 4.9186 3.0994 1.587 0.113
## op_count:arg0 -0.2255 0.1600 -1.410 0.159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 425.9 on 584 degrees of freedom
## Multiple R-squared: 0.08522, Adjusted R-squared: 0.08052
## F-statistic: 18.13 on 3 and 584 DF, p-value: 0.00000000002908
##
## [1] "DUP8" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -951.1 -315.4 -41.2 336.1 1342.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11289.1622 52.8858 213.463 < 0.0000000000000002 ***
## op_count 9.8743 2.7350 3.610 0.000332 ***
## arg0 2.1848 3.0088 0.726 0.468040
## op_count:arg0 0.1326 0.1557 0.852 0.394830
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 409.5 on 582 degrees of freedom
## Multiple R-squared: 0.1202, Adjusted R-squared: 0.1156
## F-statistic: 26.5 on 3 and 582 DF, p-value: 0.0000000000000004458
##
## [1] "DUP9" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -930.86 -344.64 -24.66 333.68 1187.14
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11225.0803 57.9886 193.574 < 0.0000000000000002 ***
## op_count 16.1835 2.9924 5.408 0.000000093 ***
## arg0 4.6469 3.1467 1.477 0.14
## op_count:arg0 -0.2403 0.1627 -1.477 0.14
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 427.6 on 583 degrees of freedom
## Multiple R-squared: 0.114, Adjusted R-squared: 0.1094
## F-statistic: 25 on 3 and 583 DF, p-value: 0.000000000000003141
##
## [1] "DUP10" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -902.07 -304.98 -15.11 317.66 1138.06
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11178.4236 55.2339 202.383 < 0.0000000000000002 ***
## op_count 17.6511 2.8489 6.196 0.0000000011 ***
## arg0 5.3139 2.7400 1.939 0.0529 .
## op_count:arg0 -0.3041 0.1411 -2.155 0.0316 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 401 on 582 degrees of freedom
## Multiple R-squared: 0.1299, Adjusted R-squared: 0.1254
## F-statistic: 28.96 on 3 and 582 DF, p-value: < 0.00000000000000022
##
## [1] "DUP11" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -921.67 -359.89 -86.21 345.37 1554.56
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11461.4654 62.3178 183.920 <0.0000000000000002 ***
## op_count 7.9161 3.2019 2.472 0.0137 *
## arg0 -4.3895 3.2490 -1.351 0.1772
## op_count:arg0 0.1736 0.1673 1.037 0.3000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 450.7 on 587 degrees of freedom
## Multiple R-squared: 0.08273, Adjusted R-squared: 0.07804
## F-statistic: 17.65 on 3 and 587 DF, p-value: 0.00000000005586
##
## [1] "DUP12" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -969.24 -317.84 -78.02 315.73 1250.23
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11245.2041 53.0851 211.833 < 0.0000000000000002 ***
## op_count 12.6893 2.7385 4.634 0.00000443 ***
## arg0 1.1859 2.8161 0.421 0.674
## op_count:arg0 -0.1794 0.1450 -1.238 0.216
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 408.1 on 584 degrees of freedom
## Multiple R-squared: 0.08302, Adjusted R-squared: 0.07831
## F-statistic: 17.62 on 3 and 584 DF, p-value: 0.00000000005791
##
## [1] "DUP13" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -882.12 -320.86 -60.29 316.23 1242.01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11296.77948 59.01261 191.430 < 0.0000000000000002 ***
## op_count 11.15892 3.04624 3.663 0.000272 ***
## arg0 -0.10124 3.03173 -0.033 0.973372
## op_count:arg0 0.05358 0.15686 0.342 0.732794
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 424.1 on 587 degrees of freedom
## Multiple R-squared: 0.1096, Adjusted R-squared: 0.1051
## F-statistic: 24.09 on 3 and 587 DF, p-value: 0.00000000000001024
##
## [1] "DUP14" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -956.65 -296.14 -72.21 297.99 1135.29
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11267.9226 51.9522 216.890 < 0.0000000000000002 ***
## op_count 9.1456 2.6789 3.414 0.000684 ***
## arg0 0.7929 2.8012 0.283 0.777225
## op_count:arg0 0.1076 0.1448 0.743 0.457848
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 393.8 on 586 degrees of freedom
## Multiple R-squared: 0.1072, Adjusted R-squared: 0.1026
## F-statistic: 23.44 on 3 and 586 DF, p-value: 0.00000000000002422
##
## [1] "DUP15" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -987.93 -326.02 -80.96 309.75 1482.71
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11246.0494 60.7845 185.015 <0.0000000000000002 ***
## op_count 8.1210 3.1482 2.580 0.0101 *
## arg0 0.7066 3.1399 0.225 0.8220
## op_count:arg0 0.0629 0.1624 0.387 0.6987
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 416.2 on 591 degrees of freedom
## Multiple R-squared: 0.07025, Adjusted R-squared: 0.06553
## F-statistic: 14.88 on 3 and 591 DF, p-value: 0.000000002363
##
## [1] "DUP16" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -942.63 -339.94 -69.57 314.65 1453.63
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11473.9828 57.4369 199.767 < 0.0000000000000002 ***
## op_count 8.8138 2.9715 2.966 0.00314 **
## arg0 -1.8670 2.9969 -0.623 0.53354
## op_count:arg0 0.1789 0.1554 1.151 0.25024
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 425.3 on 583 degrees of freedom
## Multiple R-squared: 0.1063, Adjusted R-squared: 0.1017
## F-statistic: 23.11 on 3 and 583 DF, p-value: 0.00000000000003817
##
## [1] "ADD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1002.5 -353.9 -72.4 365.6 1464.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11309.18213 82.83301 136.530 < 0.0000000000000002 ***
## op_count 27.38172 4.32405 6.332 0.000000000483 ***
## arg0 -1.27122 3.27462 -0.388 0.698
## arg1 -0.95265 3.20664 -0.297 0.767
## op_count:arg0 0.09481 0.16942 0.560 0.576
## op_count:arg1 0.14293 0.16568 0.863 0.389
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 453.2 on 582 degrees of freedom
## Multiple R-squared: 0.4227, Adjusted R-squared: 0.4177
## F-statistic: 85.23 on 5 and 582 DF, p-value: < 0.00000000000000022
##
## [1] "MUL" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1118.7 -397.7 -116.3 416.8 1661.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11396.0868 92.6551 122.995 <0.0000000000000002 ***
## op_count 64.3015 4.7852 13.438 <0.0000000000000002 ***
## arg0 -2.6233 3.6555 -0.718 0.4733
## arg1 -2.0639 3.6951 -0.559 0.5767
## op_count:arg0 0.1298 0.1896 0.684 0.4941
## op_count:arg1 -0.3135 0.1901 -1.649 0.0996 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 506.5 on 587 degrees of freedom
## Multiple R-squared: 0.6943, Adjusted R-squared: 0.6917
## F-statistic: 266.6 on 5 and 587 DF, p-value: < 0.00000000000000022
##
## [1] "SUB" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1028.9 -370.6 -105.4 418.7 1416.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11274.9161 86.1084 130.939 < 0.0000000000000002 ***
## op_count 27.8737 4.4579 6.253 0.000000000777 ***
## arg0 1.3613 3.4105 0.399 0.690
## arg1 -0.7169 3.3649 -0.213 0.831
## op_count:arg0 0.1084 0.1760 0.616 0.538
## op_count:arg1 0.1138 0.1739 0.654 0.513
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 467 on 587 degrees of freedom
## Multiple R-squared: 0.4102, Adjusted R-squared: 0.4052
## F-statistic: 81.66 on 5 and 587 DF, p-value: < 0.00000000000000022
##
## [1] "DIV" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1472.62 -371.58 -44.52 399.27 2016.13
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11305.4941 94.4679 119.676 < 0.0000000000000002 ***
## op_count 33.3647 4.8759 6.843 0.0000000000195008 ***
## arg0 1.6300 3.7770 0.432 0.666223
## arg1 -2.7608 3.6940 -0.747 0.455132
## op_count:arg0 1.4914 0.1950 7.647 0.0000000000000839 ***
## op_count:arg1 -0.6507 0.1909 -3.408 0.000698 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 537.5 on 590 degrees of freedom
## Multiple R-squared: 0.6014, Adjusted R-squared: 0.598
## F-statistic: 178 on 5 and 590 DF, p-value: < 0.00000000000000022
##
## [1] "SDIV" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1611.21 -436.66 -59.41 427.89 2337.53
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11338.5724 106.1898 106.776 < 0.0000000000000002 ***
## op_count 52.1996 5.4538 9.571 < 0.0000000000000002 ***
## arg0 -0.7147 4.3197 -0.165 0.869
## arg1 2.2316 4.3603 0.512 0.609
## op_count:arg0 1.9926 0.2218 8.982 < 0.0000000000000002 ***
## op_count:arg1 -1.1711 0.2234 -5.241 0.000000223 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 610.6 on 586 degrees of freedom
## Multiple R-squared: 0.6881, Adjusted R-squared: 0.6855
## F-statistic: 258.6 on 5 and 586 DF, p-value: < 0.00000000000000022
##
## [1] "MOD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1492.65 -369.67 -20.04 356.69 1467.16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11187.6090 91.5059 122.261 < 0.0000000000000002 ***
## op_count 38.7728 4.7191 8.216 0.00000000000000136 ***
## arg0 2.0115 3.5883 0.561 0.5753
## arg1 6.0211 3.6248 1.661 0.0972 .
## op_count:arg0 1.5284 0.1854 8.246 0.00000000000000109 ***
## op_count:arg1 -1.0541 0.1872 -5.631 0.00000002787512287 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 513 on 585 degrees of freedom
## Multiple R-squared: 0.6379, Adjusted R-squared: 0.6348
## F-statistic: 206.1 on 5 and 585 DF, p-value: < 0.00000000000000022
##
## [1] "SMOD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1679.22 -440.76 -35.28 410.03 2241.67
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11470.4703 109.5369 104.718 < 0.0000000000000002 ***
## op_count 54.1830 5.6585 9.575 < 0.0000000000000002 ***
## arg0 1.7370 4.2093 0.413 0.680
## arg1 -5.1664 4.5935 -1.125 0.261
## op_count:arg0 1.5750 0.2181 7.223 0.00000000000157 ***
## op_count:arg1 -0.5577 0.2370 -2.353 0.019 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 617.2 on 592 degrees of freedom
## Multiple R-squared: 0.6929, Adjusted R-squared: 0.6903
## F-statistic: 267.2 on 5 and 592 DF, p-value: < 0.00000000000000022
##
## [1] "EXP" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -83670 -9345 -3617 627 175645
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8482.6999 5167.1265 1.642 0.10120
## op_count 870.2124 265.4011 3.279 0.00111 **
## arg0 161.4904 221.0792 0.730 0.46540
## arg1 195.9630 214.2141 0.915 0.36068
## op_count:arg0 -0.7686 11.3090 -0.068 0.94584
## op_count:arg1 274.3254 10.9393 25.077 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31780 on 576 degrees of freedom
## Multiple R-squared: 0.8779, Adjusted R-squared: 0.8769
## F-statistic: 828.5 on 5 and 576 DF, p-value: < 0.00000000000000022
##
## [1] "SIGNEXTEND" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -901.43 -306.83 -92.76 313.97 1210.44
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11363.41359 70.30078 161.640 < 0.0000000000000002 ***
## op_count 11.98944 3.68017 3.258 0.00119 **
## arg0 -0.37670 2.81051 -0.134 0.89342
## arg1 -3.25407 2.90902 -1.119 0.26377
## op_count:arg0 -0.02723 0.14632 -0.186 0.85246
## op_count:arg1 -0.07930 0.15117 -0.525 0.60006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 406.8 on 584 degrees of freedom
## Multiple R-squared: 0.09556, Adjusted R-squared: 0.08781
## F-statistic: 12.34 on 5 and 584 DF, p-value: 0.00000000002148
##
## [1] "LT" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -894.0 -334.6 -92.2 329.0 1541.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11324.61686 82.18303 137.798 < 0.0000000000000002 ***
## op_count 21.77008 4.23100 5.145 0.000000366 ***
## arg0 0.75147 3.19714 0.235 0.814
## arg1 0.01483 3.29420 0.005 0.996
## op_count:arg0 0.09613 0.16574 0.580 0.562
## op_count:arg1 -0.12815 0.17009 -0.753 0.452
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 440.6 on 580 degrees of freedom
## Multiple R-squared: 0.2634, Adjusted R-squared: 0.257
## F-statistic: 41.48 on 5 and 580 DF, p-value: < 0.00000000000000022
##
## [1] "GT" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -952.10 -343.30 -83.57 345.04 1323.05
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11318.470330 77.184650 146.641 < 0.0000000000000002 ***
## op_count 23.564537 3.970908 5.934 0.00000000504 ***
## arg0 -1.798547 3.294410 -0.546 0.585
## arg1 0.550794 3.144619 0.175 0.861
## op_count:arg0 -0.021025 0.168513 -0.125 0.901
## op_count:arg1 0.002458 0.161713 0.015 0.988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 434.5 on 588 degrees of freedom
## Multiple R-squared: 0.3039, Adjusted R-squared: 0.298
## F-statistic: 51.35 on 5 and 588 DF, p-value: < 0.00000000000000022
##
## [1] "SLT" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -920.36 -368.54 -49.41 373.50 1232.70
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11283.17122 88.27974 127.812 < 0.0000000000000002 ***
## op_count 36.03767 4.55633 7.909 0.0000000000000129 ***
## arg0 -1.55954 3.31117 -0.471 0.638
## arg1 0.33807 3.20135 0.106 0.916
## op_count:arg0 -0.08555 0.17128 -0.500 0.618
## op_count:arg1 0.01873 0.16551 0.113 0.910
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 464.1 on 588 degrees of freedom
## Multiple R-squared: 0.4614, Adjusted R-squared: 0.4569
## F-statistic: 100.8 on 5 and 588 DF, p-value: < 0.00000000000000022
##
## [1] "SGT" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -891.61 -324.90 -87.55 338.66 1146.08
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11315.4863 75.9850 148.917 < 0.0000000000000002 ***
## op_count 25.9760 3.9388 6.595 0.0000000000952 ***
## arg0 0.5150 3.2517 0.158 0.874
## arg1 -2.3844 2.9376 -0.812 0.417
## op_count:arg0 0.1385 0.1678 0.826 0.409
## op_count:arg1 -0.0267 0.1518 -0.176 0.861
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 423.6 on 586 degrees of freedom
## Multiple R-squared: 0.3968, Adjusted R-squared: 0.3917
## F-statistic: 77.11 on 5 and 586 DF, p-value: < 0.00000000000000022
##
## [1] "EQ" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1028.64 -304.38 -78.71 311.29 1115.06
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11289.7822 69.9943 161.296 < 0.0000000000000002 ***
## op_count 19.0458 3.6091 5.277 0.000000185 ***
## arg0 0.7429 2.7952 0.266 0.790
## arg1 -2.0786 2.9345 -0.708 0.479
## op_count:arg0 -0.0243 0.1443 -0.168 0.866
## op_count:arg1 0.1303 0.1511 0.862 0.389
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 400.3 on 586 degrees of freedom
## Multiple R-squared: 0.2917, Adjusted R-squared: 0.2857
## F-statistic: 48.28 on 5 and 586 DF, p-value: < 0.00000000000000022
##
## [1] "AND" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1093.28 -379.86 -87.18 396.83 1487.03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11281.0281 81.6042 138.241 <0.0000000000000002 ***
## op_count 39.9884 4.2070 9.505 <0.0000000000000002 ***
## arg0 -3.7612 3.2575 -1.155 0.2487
## arg1 3.8716 3.5499 1.091 0.2759
## op_count:arg0 0.3209 0.1681 1.909 0.0567 .
## op_count:arg1 -0.1420 0.1828 -0.777 0.4376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 476 on 586 degrees of freedom
## Multiple R-squared: 0.5516, Adjusted R-squared: 0.5478
## F-statistic: 144.2 on 5 and 586 DF, p-value: < 0.00000000000000022
##
## [1] "OR" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -982.28 -352.71 -67.83 378.03 1370.94
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11181.969319 80.396673 139.085 <0.0000000000000002 ***
## op_count 44.799915 4.140538 10.820 <0.0000000000000002 ***
## arg0 4.344679 3.261722 1.332 0.183
## arg1 0.372761 3.243086 0.115 0.909
## op_count:arg0 -0.109781 0.167720 -0.655 0.513
## op_count:arg1 0.004406 0.167360 0.026 0.979
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 451.5 on 586 degrees of freedom
## Multiple R-squared: 0.5807, Adjusted R-squared: 0.5772
## F-statistic: 162.3 on 5 and 586 DF, p-value: < 0.00000000000000022
##
## [1] "XOR" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1114.6 -367.4 -102.5 382.1 1472.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11223.4085 83.2343 134.841 <0.0000000000000002 ***
## op_count 55.3645 4.2857 12.919 <0.0000000000000002 ***
## arg0 2.8407 3.3110 0.858 0.3913
## arg1 2.1184 3.3164 0.639 0.5232
## op_count:arg0 -0.4116 0.1705 -2.414 0.0161 *
## op_count:arg1 -0.3339 0.1710 -1.953 0.0513 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 479.3 on 583 degrees of freedom
## Multiple R-squared: 0.5525, Adjusted R-squared: 0.5486
## F-statistic: 143.9 on 5 and 583 DF, p-value: < 0.00000000000000022
##
## [1] "BYTE" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -874.64 -330.88 -72.75 342.51 1311.48
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11335.84516 78.50455 144.397 < 0.0000000000000002 ***
## op_count 18.79053 4.04814 4.642 0.00000426 ***
## arg0 -3.23404 3.12731 -1.034 0.302
## arg1 -0.36478 3.06450 -0.119 0.905
## op_count:arg0 0.10086 0.16104 0.626 0.531
## op_count:arg1 0.03258 0.15809 0.206 0.837
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 423.3 on 587 degrees of freedom
## Multiple R-squared: 0.2711, Adjusted R-squared: 0.2649
## F-statistic: 43.67 on 5 and 587 DF, p-value: < 0.00000000000000022
##
## [1] "SHL" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1091.75 -376.29 -25.72 357.92 1512.54
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11339.98628 80.96564 140.059 < 0.0000000000000002 ***
## op_count 29.47272 4.16889 7.070 0.00000000000445 ***
## arg0 -0.97946 3.20001 -0.306 0.760
## arg1 0.83671 3.31784 0.252 0.801
## op_count:arg0 -0.16253 0.16401 -0.991 0.322
## op_count:arg1 0.05299 0.17084 0.310 0.757
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 458.2 on 584 degrees of freedom
## Multiple R-squared: 0.3578, Adjusted R-squared: 0.3523
## F-statistic: 65.07 on 5 and 584 DF, p-value: < 0.00000000000000022
##
## [1] "SHR" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -989.29 -322.33 -81.34 326.56 1321.30
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11407.5160 71.4784 159.594 < 0.0000000000000002 ***
## op_count 26.9103 3.6591 7.354 0.000000000000655 ***
## arg0 0.6180 3.0806 0.201 0.841
## arg1 -4.2676 3.0393 -1.404 0.161
## op_count:arg0 -0.1067 0.1590 -0.671 0.502
## op_count:arg1 0.0704 0.1564 0.450 0.653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 431.6 on 582 degrees of freedom
## Multiple R-squared: 0.3672, Adjusted R-squared: 0.3617
## F-statistic: 67.54 on 5 and 582 DF, p-value: < 0.00000000000000022
##
## [1] "SAR" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -901.0 -340.9 -110.2 349.5 1517.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11282.3441 86.3154 130.711 <0.0000000000000002 ***
## op_count 37.5607 4.4220 8.494 <0.0000000000000002 ***
## arg0 2.3783 3.2533 0.731 0.465
## arg1 -3.0213 3.2616 -0.926 0.355
## op_count:arg0 -0.1315 0.1676 -0.785 0.433
## op_count:arg1 0.1341 0.1670 0.803 0.422
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 444.3 on 585 degrees of freedom
## Multiple R-squared: 0.5256, Adjusted R-squared: 0.5215
## F-statistic: 129.6 on 5 and 585 DF, p-value: < 0.00000000000000022
##
## [1] "MSTORE" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -886.81 -305.96 -96.56 214.06 1281.10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12095.8387882 80.2051035 150.811 <0.0000000000000002 ***
## op_count 62.0356443 4.1146172 15.077 <0.0000000000000002 ***
## arg0 0.0005848 0.0061545 0.095 0.924
## arg1 0.0009323 0.0060563 0.154 0.878
## op_count:arg0 -0.0002570 0.0003197 -0.804 0.422
## op_count:arg1 -0.0002418 0.0003124 -0.774 0.439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 445.5 on 581 degrees of freedom
## Multiple R-squared: 0.7189, Adjusted R-squared: 0.7165
## F-statistic: 297.1 on 5 and 581 DF, p-value: < 0.00000000000000022
##
## [1] "MSTORE8" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -830.87 -241.50 -33.52 188.65 998.39
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12062.7563542 61.5499487 195.983 <0.0000000000000002 ***
## op_count 52.6845823 3.2169939 16.377 <0.0000000000000002 ***
## arg0 0.0024697 0.0051255 0.482 0.630
## arg1 0.0044185 0.0051758 0.854 0.394
## op_count:arg0 -0.0001703 0.0002669 -0.638 0.524
## op_count:arg1 -0.0000789 0.0002721 -0.290 0.772
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 355.1 on 554 degrees of freedom
## Multiple R-squared: 0.7528, Adjusted R-squared: 0.7506
## F-statistic: 337.5 on 5 and 554 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP1" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -774.95 -270.06 -27.02 247.62 1018.85
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10129.8565 61.0048 166.050 < 0.0000000000000002 ***
## op_count 11.2041 3.1448 3.563 0.000397 ***
## arg0 3.3027 2.4649 1.340 0.180796
## arg1 -3.2955 2.3939 -1.377 0.169157
## op_count:arg0 -0.1633 0.1267 -1.289 0.197995
## op_count:arg1 0.1918 0.1232 1.557 0.119915
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 344.5 on 585 degrees of freedom
## Multiple R-squared: 0.1596, Adjusted R-squared: 0.1525
## F-statistic: 22.23 on 5 and 585 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP2" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -981.01 -265.96 -46.48 263.82 1180.40
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10115.36512 64.56883 156.660 < 0.0000000000000002 ***
## op_count 13.60901 3.35938 4.051 0.0000579 ***
## arg0 -1.62842 2.62969 -0.619 0.536
## arg1 0.27533 2.49282 0.110 0.912
## op_count:arg0 -0.00645 0.13592 -0.047 0.962
## op_count:arg1 -0.03866 0.12961 -0.298 0.766
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 358.1 on 582 degrees of freedom
## Multiple R-squared: 0.1653, Adjusted R-squared: 0.1582
## F-statistic: 23.06 on 5 and 582 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP3" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -904.07 -249.06 -25.81 244.96 982.13
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10167.727707 58.972965 172.413 < 0.0000000000000002 ***
## op_count 13.430916 3.066697 4.380 0.0000141 ***
## arg0 -1.502049 2.382997 -0.630 0.529
## arg1 -1.340286 2.312503 -0.580 0.562
## op_count:arg0 -0.004771 0.123897 -0.039 0.969
## op_count:arg1 -0.038174 0.119637 -0.319 0.750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 337.4 on 581 degrees of freedom
## Multiple R-squared: 0.1793, Adjusted R-squared: 0.1722
## F-statistic: 25.39 on 5 and 581 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP4" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -935.14 -258.38 -48.97 247.13 997.73
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10042.176303 61.464721 163.381 < 0.0000000000000002 ***
## op_count 13.756608 3.165897 4.345 0.0000164 ***
## arg0 -0.711194 2.496743 -0.285 0.776
## arg1 3.082594 2.393842 1.288 0.198
## op_count:arg0 -0.056723 0.129031 -0.440 0.660
## op_count:arg1 -0.003359 0.122852 -0.027 0.978
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 339.6 on 580 degrees of freedom
## Multiple R-squared: 0.1825, Adjusted R-squared: 0.1754
## F-statistic: 25.89 on 5 and 580 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP5" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -997.38 -257.02 -47.21 260.23 983.63
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10185.9608 66.4004 153.402 <0.0000000000000002 ***
## op_count 7.9710 3.4360 2.320 0.0207 *
## arg0 -1.6646 2.4658 -0.675 0.4999
## arg1 -3.1864 2.6590 -1.198 0.2313
## op_count:arg0 0.1354 0.1276 1.061 0.2891
## op_count:arg1 0.1103 0.1382 0.798 0.4250
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 350.2 on 583 degrees of freedom
## Multiple R-squared: 0.1535, Adjusted R-squared: 0.1462
## F-statistic: 21.14 on 5 and 583 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP6" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -855.48 -242.78 -46.44 241.96 1034.26
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9997.7908 63.7740 156.769 < 0.0000000000000002 ***
## op_count 16.8105 3.2877 5.113 0.000000429 ***
## arg0 3.9384 2.4200 1.627 0.1042
## arg1 4.2332 2.4106 1.756 0.0796 .
## op_count:arg0 -0.1292 0.1250 -1.034 0.3015
## op_count:arg1 -0.1297 0.1241 -1.046 0.2962
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 340.6 on 587 degrees of freedom
## Multiple R-squared: 0.1737, Adjusted R-squared: 0.1667
## F-statistic: 24.68 on 5 and 587 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP7" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -824.61 -253.89 -43.59 227.54 1024.31
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10081.400990 65.169336 154.695 < 0.0000000000000002 ***
## op_count 14.418803 3.332097 4.327 0.0000178 ***
## arg0 0.521516 2.584877 0.202 0.8402
## arg1 4.959590 2.654395 1.868 0.0622 .
## op_count:arg0 -0.084922 0.133009 -0.638 0.5234
## op_count:arg1 -0.004248 0.136967 -0.031 0.9753
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 347.3 on 582 degrees of freedom
## Multiple R-squared: 0.1824, Adjusted R-squared: 0.1754
## F-statistic: 25.97 on 5 and 582 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP8" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -810.28 -265.12 -21.86 258.73 1068.56
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10034.99334 59.74443 167.965 < 0.0000000000000002 ***
## op_count 16.82995 3.09237 5.442 0.0000000775 ***
## arg0 1.66934 2.49053 0.670 0.503
## arg1 1.80160 2.53837 0.710 0.478
## op_count:arg0 -0.05623 0.12812 -0.439 0.661
## op_count:arg1 -0.08260 0.13036 -0.634 0.527
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 350.7 on 583 degrees of freedom
## Multiple R-squared: 0.2101, Adjusted R-squared: 0.2034
## F-statistic: 31.02 on 5 and 583 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP9" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1011.76 -256.88 -30.97 283.73 1092.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10136.7932 69.5755 145.695 < 0.0000000000000002 ***
## op_count 12.4195 3.5927 3.457 0.000586 ***
## arg0 1.6596 2.7156 0.611 0.541339
## arg1 -3.7389 2.5731 -1.453 0.146729
## op_count:arg0 -0.1308 0.1403 -0.932 0.351559
## op_count:arg1 0.2010 0.1328 1.513 0.130768
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 362.3 on 587 degrees of freedom
## Multiple R-squared: 0.1814, Adjusted R-squared: 0.1744
## F-statistic: 26.02 on 5 and 587 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP10" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -789.60 -285.42 -47.74 279.61 1084.40
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10235.48653 67.53770 151.552 < 0.0000000000000002 ***
## op_count 13.79552 3.47281 3.972 0.00008 ***
## arg0 -0.04736 2.46626 -0.019 0.985
## arg1 -1.61342 2.67436 -0.603 0.547
## op_count:arg0 -0.01384 0.12679 -0.109 0.913
## op_count:arg1 0.06652 0.13812 0.482 0.630
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 357.7 on 585 degrees of freedom
## Multiple R-squared: 0.2044, Adjusted R-squared: 0.1976
## F-statistic: 30.05 on 5 and 585 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP11" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -668.06 -277.08 -52.53 263.14 1102.83
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10115.91694 61.95567 163.277 < 0.0000000000000002 ***
## op_count 9.59124 3.19922 2.998 0.00283 **
## arg0 -1.15298 2.62945 -0.438 0.66119
## arg1 -0.83567 2.57348 -0.325 0.74551
## op_count:arg0 0.18631 0.13509 1.379 0.16837
## op_count:arg1 0.08239 0.13268 0.621 0.53486
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 358.1 on 585 degrees of freedom
## Multiple R-squared: 0.1875, Adjusted R-squared: 0.1805
## F-statistic: 26.99 on 5 and 585 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP12" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -931.19 -277.34 -51.49 268.60 1105.60
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10141.573281 61.525101 164.836 < 0.0000000000000002 ***
## op_count 14.308453 3.176435 4.505 0.00000804 ***
## arg0 1.444139 2.513974 0.574 0.566
## arg1 -1.731997 2.580525 -0.671 0.502
## op_count:arg0 0.011840 0.130348 0.091 0.928
## op_count:arg1 -0.009941 0.133569 -0.074 0.941
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 366.3 on 583 degrees of freedom
## Multiple R-squared: 0.1906, Adjusted R-squared: 0.1836
## F-statistic: 27.45 on 5 and 583 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP13" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -770.99 -262.76 -30.69 244.85 1068.45
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10131.4168 58.6486 172.748 < 0.0000000000000002 ***
## op_count 13.3928 3.0432 4.401 0.0000128 ***
## arg0 -1.1950 2.4542 -0.487 0.627
## arg1 3.3428 2.4219 1.380 0.168
## op_count:arg0 0.0287 0.1274 0.225 0.822
## op_count:arg1 -0.1344 0.1262 -1.065 0.288
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 347.9 on 581 degrees of freedom
## Multiple R-squared: 0.1486, Adjusted R-squared: 0.1413
## F-statistic: 20.28 on 5 and 581 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP14" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1038.32 -283.99 -39.12 264.98 1192.13
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10099.1435 63.8292 158.221 < 0.0000000000000002 ***
## op_count 11.6296 3.2886 3.536 0.000438 ***
## arg0 3.6572 2.7506 1.330 0.184170
## arg1 -2.3028 2.6105 -0.882 0.378074
## op_count:arg0 -0.1143 0.1418 -0.806 0.420516
## op_count:arg1 0.1508 0.1345 1.121 0.262849
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 370.2 on 587 degrees of freedom
## Multiple R-squared: 0.148, Adjusted R-squared: 0.1408
## F-statistic: 20.4 on 5 and 587 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP15" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -741.25 -290.03 -51.24 268.38 1126.03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10388.72159 66.74900 155.639 <0.0000000000000002 ***
## op_count 8.73186 3.46114 2.523 0.0119 *
## arg0 -2.38781 2.58020 -0.925 0.3551
## arg1 -3.22225 2.57789 -1.250 0.2118
## op_count:arg0 0.14752 0.13346 1.105 0.2695
## op_count:arg1 0.03426 0.13383 0.256 0.7980
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 366.5 on 584 degrees of freedom
## Multiple R-squared: 0.1405, Adjusted R-squared: 0.1332
## F-statistic: 19.1 on 5 and 584 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP16" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -899.34 -276.99 -42.58 262.74 1062.05
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10133.17584 65.22558 155.356 < 0.0000000000000002 ***
## op_count 17.06940 3.37669 5.055 0.000000575 ***
## arg0 2.91406 2.47734 1.176 0.240
## arg1 2.58108 2.67459 0.965 0.335
## op_count:arg0 -0.19021 0.12860 -1.479 0.140
## op_count:arg1 -0.06497 0.13878 -0.468 0.640
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 367.4 on 588 degrees of freedom
## Multiple R-squared: 0.1631, Adjusted R-squared: 0.156
## F-statistic: 22.92 on 5 and 588 DF, p-value: < 0.00000000000000022
##
## [1] "ADDMOD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1925.41 -417.91 -73.87 455.65 2635.90
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11178.9290 141.4850 79.011 < 0.0000000000000002 ***
## op_count 60.6174 7.3035 8.300 0.000000000000000715 ***
## arg0 2.9558 4.2682 0.693 0.488881
## arg1 5.4713 4.4222 1.237 0.216494
## arg2 -3.5482 4.6692 -0.760 0.447614
## op_count:arg0 0.6398 0.2199 2.910 0.003750 **
## op_count:arg1 0.8601 0.2285 3.765 0.000183 ***
## op_count:arg2 -0.1962 0.2415 -0.812 0.416922
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 628 on 589 degrees of freedom
## Multiple R-squared: 0.7404, Adjusted R-squared: 0.7374
## F-statistic: 240 on 7 and 589 DF, p-value: < 0.00000000000000022
##
## [1] "MULMOD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2931.7 -576.6 -111.0 605.4 4025.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11260.96110 185.47167 60.715 < 0.0000000000000002 ***
## op_count 134.02309 9.54062 14.048 < 0.0000000000000002 ***
## arg0 4.73408 6.08548 0.778 0.436924
## arg1 6.35487 6.29910 1.009 0.313461
## arg2 -1.57008 6.16816 -0.255 0.799163
## op_count:arg0 1.19757 0.31318 3.824 0.000145 ***
## op_count:arg1 1.51056 0.32509 4.647 0.00000417 ***
## op_count:arg2 0.06493 0.31881 0.204 0.838679
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 900.7 on 586 degrees of freedom
## Multiple R-squared: 0.8609, Adjusted R-squared: 0.8593
## F-statistic: 518.2 on 7 and 586 DF, p-value: < 0.00000000000000022
##
## [1] "CALLDATACOPY" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1066.5 -372.9 -101.8 177.5 2206.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12109.7662042 113.5654203 106.633 <0.0000000000000002 ***
## op_count 121.9291536 5.8569415 20.818 <0.0000000000000002 ***
## arg0 0.0021305 0.0079602 0.268 0.789
## arg1 -0.0021722 0.0082100 -0.265 0.791
## arg2 -0.0018819 0.0075485 -0.249 0.803
## op_count:arg0 -0.0001911 0.0004117 -0.464 0.643
## op_count:arg1 0.0004553 0.0004237 1.075 0.283
## op_count:arg2 0.0033309 0.0003912 8.515 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 570.6 on 577 degrees of freedom
## Multiple R-squared: 0.9164, Adjusted R-squared: 0.9154
## F-statistic: 903.8 on 7 and 577 DF, p-value: < 0.00000000000000022
##
## [1] "CODECOPY" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1527.8 -500.5 -145.6 451.8 2099.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12154.9512929 157.8618622 76.997 < 0.0000000000000002 ***
## op_count 148.3869388 8.1626820 18.179 < 0.0000000000000002 ***
## arg0 -0.0053936 0.0101918 -0.529 0.597
## arg1 -0.0015672 0.0100409 -0.156 0.876
## arg2 0.0051780 0.0102343 0.506 0.613
## op_count:arg0 0.0005368 0.0005271 1.018 0.309
## op_count:arg1 -0.0023355 0.0005228 -4.468 0.000009512 ***
## op_count:arg2 0.0026838 0.0005290 5.073 0.000000528 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 721.6 on 579 degrees of freedom
## Multiple R-squared: 0.8787, Adjusted R-squared: 0.8772
## F-statistic: 599 on 7 and 579 DF, p-value: < 0.00000000000000022
##
## [1] "RETURNDATACOPY" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2749.6 -643.9 -217.0 686.0 3947.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21122.4488465 176.9122385 119.395 <0.0000000000000002 ***
## op_count 144.9330760 9.0938990 15.937 <0.0000000000000002 ***
## arg0 0.0002961 0.0138644 0.021 0.983
## arg1 -0.0001804 0.0127753 -0.014 0.989
## arg2 -0.0070687 0.0124295 -0.569 0.570
## op_count:arg0 -0.0002008 0.0007140 -0.281 0.779
## op_count:arg1 -0.0002799 0.0006579 -0.425 0.671
## op_count:arg2 0.0063981 0.0006410 9.982 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 925.4 on 583 degrees of freedom
## Multiple R-squared: 0.8746, Adjusted R-squared: 0.8731
## F-statistic: 581 on 7 and 583 DF, p-value: < 0.00000000000000022
##
## [1] "DIV" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1426.03 -374.64 -67.92 377.02 2074.51
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11116.986 61.303 181.345 < 0.0000000000000002 ***
## op_count 29.712 2.162 13.741 < 0.0000000000000002 ***
## arg0 6.994 2.594 2.696 0.00722 **
## arg1 3.277 2.505 1.308 0.19129
## op_count:expensiveTRUE 35.415 2.753 12.866 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 502.3 on 591 degrees of freedom
## Multiple R-squared: 0.6512, Adjusted R-squared: 0.6489
## F-statistic: 275.9 on 4 and 591 DF, p-value: < 0.00000000000000022
##
## [1] "MOD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1159.66 -366.80 -66.59 389.35 1332.15
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11071.627 60.015 184.482 < 0.0000000000000002 ***
## op_count 29.161 2.162 13.489 < 0.0000000000000002 ***
## arg0 7.624 2.498 3.053 0.00237 **
## arg1 7.202 2.504 2.876 0.00418 **
## op_count:expensiveTRUE 36.304 2.685 13.521 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 484.1 on 586 degrees of freedom
## Multiple R-squared: 0.677, Adjusted R-squared: 0.6748
## F-statistic: 307 on 4 and 586 DF, p-value: < 0.00000000000000022
##
## [1] "SDIV" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1615.51 -439.84 -76.37 409.29 2831.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11160.941 69.666 160.207 <0.0000000000000002 ***
## op_count 44.396 2.523 17.598 <0.0000000000000002 ***
## arg0 7.248 3.045 2.380 0.0176 *
## arg1 4.872 2.997 1.626 0.1046
## op_count:expensiveTRUE 43.153 3.184 13.555 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 576.9 on 587 degrees of freedom
## Multiple R-squared: 0.7212, Adjusted R-squared: 0.7193
## F-statistic: 379.6 on 4 and 587 DF, p-value: < 0.00000000000000022
##
## [1] "SMOD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2121.2 -447.5 3.9 421.7 2214.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11252.369 70.804 158.922 <0.0000000000000002 ***
## op_count 52.326 2.406 21.752 <0.0000000000000002 ***
## arg0 6.386 2.911 2.194 0.0286 *
## arg1 3.804 3.043 1.250 0.2117
## op_count:expensiveTRUE 39.268 3.147 12.477 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 575.7 on 593 degrees of freedom
## Multiple R-squared: 0.7324, Adjusted R-squared: 0.7306
## F-statistic: 405.7 on 4 and 593 DF, p-value: < 0.00000000000000022
##
## [1] "ADDMOD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1755.1 -432.0 -51.2 416.0 3256.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10929.062 87.857 124.396 <0.0000000000000002 ***
## op_count 54.751 3.171 17.268 <0.0000000000000002 ***
## arg0 3.454 2.605 1.326 0.1854
## arg1 8.878 2.707 3.279 0.0011 **
## arg2 7.571 2.984 2.537 0.0114 *
## op_count:expensiveTRUE 38.587 3.367 11.462 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 578.8 on 591 degrees of freedom
## Multiple R-squared: 0.7788, Adjusted R-squared: 0.7769
## F-statistic: 416 on 5 and 591 DF, p-value: < 0.00000000000000022
##
## [1] "MULMOD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2006.8 -584.5 -110.8 655.5 3515.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10726.418 121.670 88.160 < 0.0000000000000002 ***
## op_count 137.089 5.468 25.073 < 0.0000000000000002 ***
## arg0 10.483 3.927 2.669 0.007810 **
## arg1 18.468 3.996 4.621 0.00000469 ***
## arg2 13.793 4.070 3.389 0.000748 ***
## op_count:expensiveTRUE 51.383 5.644 9.104 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 866.7 on 588 degrees of freedom
## Multiple R-squared: 0.8708, Adjusted R-squared: 0.8697
## F-statistic: 792.5 on 5 and 588 DF, p-value: < 0.00000000000000022proceed_with_opcodes = unique(first_pass[which(first_pass$has_impacting == 'TRUE'), 'opcode'])
models_with_args_automatic = first_pass[which(first_pass$has_impacting == 'TRUE'), c('opcode', 'env')]
models_with_expensive_automatic = first_pass[which(!is.na(first_pass$expensive_ns)), c('opcode', 'env')]
first_pass[which(first_pass$has_impacting == 'TRUE'), ]## opcode env has_significant has_impacting estimate_marginal_ns arg0_ns
## 30 EXP nethermind TRUE TRUE 870.212425320273 <NA>
## arg1_ns arg2_ns expensive_ns arg0_ns_raw arg1_ns_raw
## 30 274.325423327779 <NA> <NA> -0.768641020506283 274.325423327779
## arg2_ns_raw expensive_ns_raw arg0_ns_p
## 30 <NA> <NA> 0.945835425750283
## arg1_ns_p
## 30 0.000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000226222424685711
## arg2_ns_p expensive_ns_p
## 30 <NA> <NA>We inspect the automatic choice of models, but then coerce the choice
to a fixed list. We drop the division OPCODEs (DIV etc.),
because their arguments only seem to have an indirect impact via the
fact that x / y is trivial if x < y. This makes the
DIV(x, y) appear costlier for large x and cheaper for large
y.
models_with_args = data.frame(opcode="EXP", env=env, arg=1)
first_pass$arg1_ns[is.na(first_pass$arg1_ns) & first_pass$opcode=="EXP" & first_pass$env==env] <- first_pass$arg1_ns_raw[is.na(first_pass$arg1_ns) & first_pass$opcode=="EXP" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="CALLDATACOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="CALLDATACOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="CALLDATACOPY" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="CODECOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="CODECOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="CODECOPY" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="RETURNDATACOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="RETURNDATACOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="RETURNDATACOPY" & first_pass$env==env]
models_with_expensive = data.frame(opcode="DIV", env=env)
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="SDIV", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="MOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="SMOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="ADDMOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="MULMOD", env=env))Detailed analysis for selected OPCODEs
We go through all the OPCODEs which turned out to have impacting arguments in the automatic discrimination procedure, and we plot some validation plots to inspect these relationships.
# Takes the results data frame and checks which argument indices (0, 1, etc.)
# turned out to be impacting
get_impact_args_for <- function(df, opcode, env) {
if (opcode %in% nullary_opcodes) {
return(c())
}
args = c()
for (n in 0:2) {
argname = paste0('arg', n, '_ns')
if (!is.na(df[which(df$opcode==opcode & df$env==env), argname])) {
args = c(n, args)
}
}
return(rev(args))
}
# same as `get_impact_args_for` but gets all the argument indices
get_args_for <- function(df, opcode, env) {
if (opcode %in% unary_opcodes) {
c(0)
} else if (opcode %in% binary_opcodes) {
c(0, 1)
} else if (opcode %in% ternary_opcodes) {
c(0, 1, 2)
}
}
# Builds a final model formula to estimate, based on whether the arguments
# came out impactful from the automatic discrimination process.
get_model_formula_for <- function(df, opcode, env) {
args = get_args_for(df, opcode, env)
argnames = paste0('arg', args)
args_formula = paste0(argnames, collapse=' + ')
impact_args = get_impact_args_for(df, opcode, env)
if (opcode %in% nullary_opcodes) {
as.formula('measure_total_time_ns ~ op_count')
} else if (is.null(impact_args)) {
as.formula(paste0('measure_total_time_ns ~ op_count + ', args_formula))
} else {
arg_op_count_names = paste0('arg', impact_args, ':op_count')
arg_op_counts_formula = paste0(arg_op_count_names, collapse=' + ')
as.formula(paste0('measure_total_time_ns ~ op_count + ', args_formula, ' + ', arg_op_counts_formula))
}
}
# Same as `get_model_formula_for` but gauged towards the division OPCODEs specifically.
get_expensive_model_formula_for <- function(df, opcode, env) {
args = get_args_for(df, opcode, env)
argnames = paste0('arg', args)
args_formula = paste0(argnames, collapse=' + ')
as.formula(paste0('measure_total_time_ns ~ op_count + ', args_formula, ' + expensive:op_count'))
}
# Same as `get_model_formula_for` but returns the formula to provide the `aggregate` function with.
get_aggregate_formula_for <- function(df, opcode, env) {
args = get_args_for(df, opcode, env)
argnames = paste0('arg', args)
args_formula = paste0(argnames, collapse=' * ')
as.formula(paste0('measure_total_time_ns ~ op_count * env * opcode * ', args_formula))
}
# Presents the diagnostic plots for a given slice of the data
plot_model <- function(df, opcode, env, use_mean) {
if (missing(use_mean)) {
use_mean = FALSE
}
if (use_mean) {
df = aggregate(get_aggregate_formula_for(df, opcode, env), measurements[which(df$opcode==opcode & df$env==env), ], mean, na.action=na.pass)
}
model = arg_lm(df, opcode, env, get_model_formula_for(first_pass, opcode, env))
print(c(opcode, env))
print(summary(model))
par(mfrow=c(2,2))
plot(model)
plot_data = df[which(df$env == env & df$opcode == opcode & df$op_count == max(df$op_count)), ]
if (opcode %in% binary_opcodes) {
par(mfrow=c(1,1))
decreasing_colors = heat.colors(nrow(plot_data))
plot_data=plot_data[order(plot_data$measure_total_time_ns, decreasing=TRUE), ]
with(plot_data, plot(arg0, arg1, col=decreasing_colors, pch=19))
}
title(main=paste(opcode, env))
}Using the functions defined above, we proceed to plot the diagnostic plots of the arguments models.
for (env in all_envs) {
for (opcode in proceed_with_opcodes) {
plot_model(measurements, opcode, env, use_mean=TRUE)
}
}## [1] "EXP" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -80670 -8378 -3498 715 175313
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8975.14 4628.14 1.939 0.053 .
## op_count 950.88 210.95 4.508 0.00000814 ***
## arg0 89.25 144.22 0.619 0.536
## arg1 220.28 219.59 1.003 0.316
## op_count:arg1 268.89 11.20 24.015 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30600 on 511 degrees of freedom
## Multiple R-squared: 0.8841, Adjusted R-squared: 0.8832
## F-statistic: 974.6 on 4 and 511 DF, p-value: < 0.00000000000000022
Producing the final estimates
We’d like to only estimate using the arg-variables in models, where this actually matters to avoid spurious impact of insignificant variables.
We’ll estimate a model with only those argument variables, where they turned out impacting. For those where no argument variable was impacting, we’ll only estimate the marginal increase (corresponding to the constant cost of an OPCODE).
# `results_df` is assumed to have the columns as the `estimates` data frame has (see below)
add_non_arg_model_estimates <- function(model, results_df, env, opcode) {
pure_op_count_coeff = summary(model)$coefficients["op_count", 1]
args_ns = c(NA, NA, NA)
args_ns_stderr = c(NA, NA, NA)
results_df[nrow(results_df) + 1, ] = c(opcode, env, FALSE, FALSE, pure_op_count_coeff, args_ns, NA, args_ns_stderr, NA)
return(results_df)
}
add_arg_model_estimates <- function(model, opcode, env, results_df, df) {
all_coefficients = summary(model)$coefficients
arg_coefficients = all_coefficients[!(row.names(all_coefficients) %in% c("op_count", "(Intercept)", "arg0", "arg1", "arg2")),]
pure_op_count_coeff = all_coefficients["op_count", 1]
# will be filled if any is impacting
args_ns = c(NA, NA, NA)
args_ns_stderr = c(NA, NA, NA)
impact_args = get_impact_args_for(df, opcode, env)
arg_op_count_names = paste0('op_count:arg', impact_args)
args_ns[impact_args + 1] = all_coefficients[arg_op_count_names, 'Estimate']
args_ns_stderr[impact_args + 1] = all_coefficients[arg_op_count_names, 'Std. Error']
results_df[nrow(results_df) + 1, ] = c(opcode, env, TRUE, TRUE, pure_op_count_coeff, args_ns, NA, args_ns_stderr, NA)
return(results_df)
}
add_expensive_model_estimates <- function(model, opcode, env, results_df, df) {
all_coefficients = summary(model)$coefficients
pure_op_count_coeff = all_coefficients["op_count", 1]
args_ns = c(NA, NA, NA)
args_ns_stderr = c(NA, NA, NA)
expensive = all_coefficients['op_count:expensiveTRUE', 'Estimate']
expensive_stderr = all_coefficients['op_count:expensiveTRUE', 'Std. Error']
results_df[nrow(results_df) + 1, ] = c(opcode, env, TRUE, TRUE, pure_op_count_coeff, args_ns, expensive, args_ns_stderr, expensive_stderr)
return(results_df)
}estimates = data.frame(matrix(ncol = 13, nrow = 0))
colnames(estimates) <- c('opcode', 'env', 'has_significant', 'has_impacting', 'estimate_marginal_ns',
'arg0_ns', 'arg1_ns', 'arg2_ns', 'expensive_ns', 'arg0_ns_stderr', 'arg1_ns_stderr', 'arg2_ns_stderr', 'expensive_ns_stderr')
for (env in all_envs) {
for (opcode in all_opcodes) {
is_modeled_with_args = nrow(merge(data.frame(opcode=opcode, env=env), models_with_args)) > 0
is_modeled_with_expensive = nrow(merge(data.frame(opcode=opcode, env=env), models_with_expensive)) > 0
if (is_modeled_with_expensive) {
model = arg_lm(measurements, opcode, env, get_expensive_model_formula_for(first_pass, opcode, env))
estimates = add_expensive_model_estimates(model, opcode, env, estimates, first_pass)
} else if (is_modeled_with_args) {
model = arg_lm(measurements, opcode, env, get_model_formula_for(first_pass, opcode, env))
estimates = add_arg_model_estimates(model, opcode, env, estimates, first_pass)
} else {
model = arg_lm(measurements, opcode, env, get_model_formula_for(first_pass, opcode, env))
estimates = add_non_arg_model_estimates(model, estimates, env, opcode)
}
print(c(opcode, env))
print(summary(model))
}
}## [1] "ADD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1013.06 -354.64 -72.61 356.52 1496.91
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11249.0211 57.2431 196.513 <0.0000000000000002 ***
## op_count 31.4480 1.5249 20.623 <0.0000000000000002 ***
## arg0 0.1237 2.0729 0.060 0.952
## arg1 1.1768 2.0249 0.581 0.561
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 452.9 on 584 degrees of freedom
## Multiple R-squared: 0.4216, Adjusted R-squared: 0.4187
## F-statistic: 141.9 on 3 and 584 DF, p-value: < 0.00000000000000022
##
## [1] "MUL" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1191.5 -406.2 -103.0 414.8 1661.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11434.540 64.007 178.646 < 0.0000000000000002 ***
## op_count 61.779 1.701 36.311 < 0.0000000000000002 ***
## arg0 -0.695 2.323 -0.299 0.76494
## arg1 -6.802 2.329 -2.920 0.00363 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 507.2 on 589 degrees of freedom
## Multiple R-squared: 0.6925, Adjusted R-squared: 0.6909
## F-statistic: 442.1 on 3 and 589 DF, p-value: < 0.00000000000000022
##
## [1] "SUB" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1028.54 -366.16 -94.46 416.42 1415.87
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11220.6278 59.2215 189.469 <0.0000000000000002 ***
## op_count 31.5027 1.5629 20.156 <0.0000000000000002 ***
## arg0 2.9867 2.1507 1.389 0.165
## arg1 0.9828 2.1295 0.462 0.645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 466.5 on 589 degrees of freedom
## Multiple R-squared: 0.4095, Adjusted R-squared: 0.4065
## F-statistic: 136.1 on 3 and 589 DF, p-value: < 0.00000000000000022
##
## [1] "DIV" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1426.03 -374.64 -67.92 377.02 2074.51
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11116.986 61.303 181.345 < 0.0000000000000002 ***
## op_count 29.712 2.162 13.741 < 0.0000000000000002 ***
## arg0 6.994 2.594 2.696 0.00722 **
## arg1 3.277 2.505 1.308 0.19129
## op_count:expensiveTRUE 35.415 2.753 12.866 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 502.3 on 591 degrees of freedom
## Multiple R-squared: 0.6512, Adjusted R-squared: 0.6489
## F-statistic: 275.9 on 4 and 591 DF, p-value: < 0.00000000000000022
##
## [1] "SDIV" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1615.51 -439.84 -76.37 409.29 2831.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11160.941 69.666 160.207 <0.0000000000000002 ***
## op_count 44.396 2.523 17.598 <0.0000000000000002 ***
## arg0 7.248 3.045 2.380 0.0176 *
## arg1 4.872 2.997 1.626 0.1046
## op_count:expensiveTRUE 43.153 3.184 13.555 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 576.9 on 587 degrees of freedom
## Multiple R-squared: 0.7212, Adjusted R-squared: 0.7193
## F-statistic: 379.6 on 4 and 587 DF, p-value: < 0.00000000000000022
##
## [1] "MOD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1159.66 -366.80 -66.59 389.35 1332.15
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11071.627 60.015 184.482 < 0.0000000000000002 ***
## op_count 29.161 2.162 13.489 < 0.0000000000000002 ***
## arg0 7.624 2.498 3.053 0.00237 **
## arg1 7.202 2.504 2.876 0.00418 **
## op_count:expensiveTRUE 36.304 2.685 13.521 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 484.1 on 586 degrees of freedom
## Multiple R-squared: 0.677, Adjusted R-squared: 0.6748
## F-statistic: 307 on 4 and 586 DF, p-value: < 0.00000000000000022
##
## [1] "SMOD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2121.2 -447.5 3.9 421.7 2214.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11252.369 70.804 158.922 <0.0000000000000002 ***
## op_count 52.326 2.406 21.752 <0.0000000000000002 ***
## arg0 6.386 2.911 2.194 0.0286 *
## arg1 3.804 3.043 1.250 0.2117
## op_count:expensiveTRUE 39.268 3.147 12.477 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 575.7 on 593 degrees of freedom
## Multiple R-squared: 0.7324, Adjusted R-squared: 0.7306
## F-statistic: 405.7 on 4 and 593 DF, p-value: < 0.00000000000000022
##
## [1] "ADDMOD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1755.1 -432.0 -51.2 416.0 3256.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10929.062 87.857 124.396 <0.0000000000000002 ***
## op_count 54.751 3.171 17.268 <0.0000000000000002 ***
## arg0 3.454 2.605 1.326 0.1854
## arg1 8.878 2.707 3.279 0.0011 **
## arg2 7.571 2.984 2.537 0.0114 *
## op_count:expensiveTRUE 38.587 3.367 11.462 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 578.8 on 591 degrees of freedom
## Multiple R-squared: 0.7788, Adjusted R-squared: 0.7769
## F-statistic: 416 on 5 and 591 DF, p-value: < 0.00000000000000022
##
## [1] "MULMOD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2006.8 -584.5 -110.8 655.5 3515.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10726.418 121.670 88.160 < 0.0000000000000002 ***
## op_count 137.089 5.468 25.073 < 0.0000000000000002 ***
## arg0 10.483 3.927 2.669 0.007810 **
## arg1 18.468 3.996 4.621 0.00000469 ***
## arg2 13.793 4.070 3.389 0.000748 ***
## op_count:expensiveTRUE 51.383 5.644 9.104 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 866.7 on 588 degrees of freedom
## Multiple R-squared: 0.8708, Adjusted R-squared: 0.8697
## F-statistic: 792.5 on 5 and 588 DF, p-value: < 0.00000000000000022
##
## [1] "EXP" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -83812 -9327 -3644 563 175680
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8654.71 4501.06 1.923 0.055 .
## op_count 858.79 205.26 4.184 0.0000331 ***
## arg0 149.89 140.35 1.068 0.286
## arg1 197.02 213.46 0.923 0.356
## op_count:arg1 274.26 10.88 25.199 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31760 on 577 degrees of freedom
## Multiple R-squared: 0.8779, Adjusted R-squared: 0.8771
## F-statistic: 1037 on 4 and 577 DF, p-value: < 0.00000000000000022
##
## [1] "SIGNEXTEND" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -898.95 -302.50 -93.68 315.66 1217.16
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11389.6226 48.9540 232.660 < 0.0000000000000002 ***
## op_count 10.2144 1.3678 7.468 0.000000000000297 ***
## arg0 -0.7763 1.7860 -0.435 0.6640
## arg1 -4.4306 1.8467 -2.399 0.0167 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 406.2 on 586 degrees of freedom
## Multiple R-squared: 0.09507, Adjusted R-squared: 0.09044
## F-statistic: 20.52 on 3 and 586 DF, p-value: 0.000000000001176
##
## [1] "LT" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -917.34 -337.52 -86.78 333.03 1536.89
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11331.962 56.405 200.902 <0.0000000000000002 ***
## op_count 21.287 1.486 14.324 <0.0000000000000002 ***
## arg0 2.170 2.031 1.069 0.286
## arg1 -1.899 2.080 -0.913 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 440.2 on 582 degrees of freedom
## Multiple R-squared: 0.2622, Adjusted R-squared: 0.2584
## F-statistic: 68.94 on 3 and 582 DF, p-value: < 0.00000000000000022
##
## [1] "GT" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -953.46 -343.38 -85.83 343.26 1323.00
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11323.0881 53.3653 212.181 <0.0000000000000002 ***
## op_count 23.2610 1.4532 16.007 <0.0000000000000002 ***
## arg0 -2.1198 2.0522 -1.033 0.302
## arg1 0.5904 1.9762 0.299 0.765
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 433.7 on 590 degrees of freedom
## Multiple R-squared: 0.3039, Adjusted R-squared: 0.3004
## F-statistic: 85.86 on 3 and 590 DF, p-value: < 0.00000000000000022
##
## [1] "SLT" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -938.19 -373.26 -55.34 377.70 1232.67
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11300.7127 60.4447 186.960 <0.0000000000000002 ***
## op_count 34.8653 1.5545 22.429 <0.0000000000000002 ***
## arg0 -2.8395 2.0922 -1.357 0.175
## arg1 0.6169 2.0209 0.305 0.760
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 463.4 on 590 degrees of freedom
## Multiple R-squared: 0.4612, Adjusted R-squared: 0.4584
## F-statistic: 168.3 on 3 and 590 DF, p-value: < 0.00000000000000022
##
## [1] "SGT" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -863.77 -328.63 -85.56 337.71 1146.15
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11289.335 52.591 214.662 <0.0000000000000002 ***
## op_count 27.728 1.419 19.535 <0.0000000000000002 ***
## arg0 2.599 2.052 1.266 0.206
## arg1 -2.794 1.856 -1.505 0.133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 423.1 on 588 degrees of freedom
## Multiple R-squared: 0.3961, Adjusted R-squared: 0.393
## F-statistic: 128.6 on 3 and 588 DF, p-value: < 0.00000000000000022
##
## [1] "EQ" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1033.03 -305.55 -66.07 311.59 1129.31
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11263.0629 48.6760 231.388 <0.0000000000000002 ***
## op_count 20.8258 1.3413 15.527 <0.0000000000000002 ***
## arg0 0.3735 1.7680 0.211 0.833
## arg1 -0.1186 1.8542 -0.064 0.949
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 399.9 on 588 degrees of freedom
## Multiple R-squared: 0.2908, Adjusted R-squared: 0.2872
## F-statistic: 80.37 on 3 and 588 DF, p-value: < 0.00000000000000022
##
## [1] "ISZERO" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1165.08 -326.91 -86.64 337.00 1268.17
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11222.595 41.547 270.117 <0.0000000000000002 ***
## op_count 20.234 1.440 14.054 <0.0000000000000002 ***
## arg0 0.179 1.960 0.091 0.927
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 428.1 on 588 degrees of freedom
## Multiple R-squared: 0.2515, Adjusted R-squared: 0.2489
## F-statistic: 98.76 on 2 and 588 DF, p-value: < 0.00000000000000022
##
## [1] "AND" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1048.38 -378.81 -93.21 408.77 1487.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11239.906 57.069 196.952 <0.0000000000000002 ***
## op_count 42.719 1.600 26.704 <0.0000000000000002 ***
## arg0 1.063 2.062 0.515 0.606
## arg1 1.719 2.239 0.768 0.443
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 476.9 on 588 degrees of freedom
## Multiple R-squared: 0.5485, Adjusted R-squared: 0.5462
## F-statistic: 238.1 on 3 and 588 DF, p-value: < 0.00000000000000022
##
## [1] "OR" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -987.29 -350.71 -70.27 372.71 1373.30
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11206.9722 55.5193 201.857 <0.0000000000000002 ***
## op_count 43.1459 1.5145 28.488 <0.0000000000000002 ***
## arg0 2.6844 2.0478 1.311 0.19
## arg1 0.4383 2.0465 0.214 0.83
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 450.9 on 588 degrees of freedom
## Multiple R-squared: 0.5804, Adjusted R-squared: 0.5783
## F-statistic: 271.2 on 3 and 588 DF, p-value: < 0.00000000000000022
##
## [1] "XOR" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1111.1 -374.0 -101.6 395.5 1471.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11413.319 58.449 195.270 <0.0000000000000002 ***
## op_count 42.726 1.621 26.358 <0.0000000000000002 ***
## arg0 -3.354 2.106 -1.593 0.112
## arg1 -2.885 2.118 -1.362 0.174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 482.6 on 585 degrees of freedom
## Multiple R-squared: 0.5446, Adjusted R-squared: 0.5423
## F-statistic: 233.2 on 3 and 585 DF, p-value: < 0.00000000000000022
##
## [1] "NOT" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1062.2 -337.4 -106.1 362.1 1535.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11226.875 45.568 246.375 <0.0000000000000002 ***
## op_count 32.358 1.513 21.388 <0.0000000000000002 ***
## arg0 1.937 2.035 0.952 0.342
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 449.3 on 585 degrees of freedom
## Multiple R-squared: 0.4392, Adjusted R-squared: 0.4373
## F-statistic: 229.1 on 2 and 585 DF, p-value: < 0.00000000000000022
##
## [1] "BYTE" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -874.52 -329.95 -73.92 341.87 1311.65
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11302.9390 53.9865 209.366 <0.0000000000000002 ***
## op_count 20.9830 1.4217 14.759 <0.0000000000000002 ***
## arg0 -1.7134 1.9633 -0.873 0.383
## arg1 0.1168 1.9274 0.061 0.952
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 422.7 on 589 degrees of freedom
## Multiple R-squared: 0.2706, Adjusted R-squared: 0.2669
## F-statistic: 72.84 on 3 and 589 DF, p-value: < 0.00000000000000022
##
## [1] "SHL" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1092.17 -376.27 -21.84 356.02 1513.14
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11367.693 55.963 203.130 <0.0000000000000002 ***
## op_count 27.643 1.542 17.929 <0.0000000000000002 ***
## arg0 -3.456 2.003 -1.725 0.085 .
## arg1 1.650 2.084 0.792 0.429
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 457.9 on 586 degrees of freedom
## Multiple R-squared: 0.3566, Adjusted R-squared: 0.3533
## F-statistic: 108.3 on 3 and 586 DF, p-value: < 0.00000000000000022
##
## [1] "SHR" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1003.46 -316.44 -83.65 326.01 1330.13
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11413.6468 50.0241 228.163 <0.0000000000000002 ***
## op_count 26.4995 1.4499 18.277 <0.0000000000000002 ***
## arg0 -0.9766 1.9462 -0.502 0.616
## arg1 -3.2177 1.9185 -1.677 0.094 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 431.1 on 584 degrees of freedom
## Multiple R-squared: 0.3665, Adjusted R-squared: 0.3632
## F-statistic: 112.6 on 3 and 584 DF, p-value: < 0.00000000000000022
##
## [1] "SAR" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -915.6 -340.7 -103.7 353.4 1549.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11276.9266 58.7186 192.050 <0.0000000000000002 ***
## op_count 37.8914 1.4898 25.434 <0.0000000000000002 ***
## arg0 0.4016 2.0530 0.196 0.845
## arg1 -0.9820 2.0459 -0.480 0.631
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 444.1 on 587 degrees of freedom
## Multiple R-squared: 0.5244, Adjusted R-squared: 0.522
## F-statistic: 215.8 on 3 and 587 DF, p-value: < 0.00000000000000022
##
## [1] "ADDRESS" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -775.06 -193.15 0.13 192.17 858.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8570.2906 18.8799 453.9 <0.0000000000000002 ***
## op_count 19.2978 0.9748 19.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 287.6 on 581 degrees of freedom
## Multiple R-squared: 0.4028, Adjusted R-squared: 0.4018
## F-statistic: 391.9 on 1 and 581 DF, p-value: < 0.00000000000000022
##
## [1] "ORIGIN" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -812.29 -186.22 -2.34 195.80 783.44
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8586.330 19.203 447.15 <0.0000000000000002 ***
## op_count 20.108 0.992 20.27 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 293.8 on 584 degrees of freedom
## Multiple R-squared: 0.413, Adjusted R-squared: 0.412
## F-statistic: 410.9 on 1 and 584 DF, p-value: < 0.00000000000000022
##
## [1] "CALLER" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -627.77 -188.41 -20.46 187.21 787.51
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8579.1817 17.8131 481.62 <0.0000000000000002 ***
## op_count 18.5808 0.9254 20.08 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 273.8 on 584 degrees of freedom
## Multiple R-squared: 0.4084, Adjusted R-squared: 0.4074
## F-statistic: 403.1 on 1 and 584 DF, p-value: < 0.00000000000000022
##
## [1] "CALLVALUE" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -723.45 -179.06 -7.38 159.27 734.44
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8611.1245 17.7041 486.4 <0.0000000000000002 ***
## op_count 10.1893 0.9178 11.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 269.8 on 573 degrees of freedom
## Multiple R-squared: 0.177, Adjusted R-squared: 0.1756
## F-statistic: 123.3 on 1 and 573 DF, p-value: < 0.00000000000000022
##
## [1] "CALLDATALOAD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -987.0 -298.6 -102.9 220.3 1231.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13151.912402 45.322417 290.186 <0.0000000000000002 ***
## op_count 28.200336 1.535237 18.369 <0.0000000000000002 ***
## arg0 0.001654 0.003908 0.423 0.672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 454.8 on 582 degrees of freedom
## Multiple R-squared: 0.3672, Adjusted R-squared: 0.365
## F-statistic: 168.9 on 2 and 582 DF, p-value: < 0.00000000000000022
##
## [1] "CALLDATASIZE" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -673.42 -186.49 -17.61 152.53 795.04
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8576.1662 16.9293 506.59 <0.0000000000000002 ***
## op_count 16.8559 0.8788 19.18 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 257.6 on 574 degrees of freedom
## Multiple R-squared: 0.3906, Adjusted R-squared: 0.3895
## F-statistic: 367.9 on 1 and 574 DF, p-value: < 0.00000000000000022
##
## [1] "CALLDATACOPY" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1075.84 -365.98 -94.25 191.59 2229.93
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12072.8477292 90.8205385 132.931 < 0.0000000000000002 ***
## op_count 124.4101719 3.6890268 33.724 < 0.0000000000000002 ***
## arg0 -0.0007107 0.0050408 -0.141 0.888
## arg1 0.0046291 0.0052196 0.887 0.376
## arg2 -0.0013087 0.0075252 -0.174 0.862
## op_count:arg2 0.0032906 0.0003892 8.456 0.000000000000000226 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 570.2 on 579 degrees of freedom
## Multiple R-squared: 0.9162, Adjusted R-squared: 0.9155
## F-statistic: 1267 on 5 and 579 DF, p-value: < 0.00000000000000022
##
## [1] "CODESIZE" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -741.6 -176.2 0.2 173.7 764.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8592.5514 17.9462 478.80 <0.0000000000000002 ***
## op_count 15.4627 0.9283 16.66 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 275 on 576 degrees of freedom
## Multiple R-squared: 0.3251, Adjusted R-squared: 0.3239
## F-statistic: 277.4 on 1 and 576 DF, p-value: < 0.00000000000000022
##
## [1] "CODECOPY" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1424.3 -556.6 -151.9 473.0 2348.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12404.2111089 125.6352091 98.732 < 0.0000000000000002 ***
## op_count 131.6384383 4.9636125 26.521 < 0.0000000000000002 ***
## arg0 0.0024251 0.0065395 0.371 0.711
## arg1 -0.0361202 0.0064877 -5.568 0.0000000395 ***
## arg2 0.0007898 0.0103552 0.076 0.939
## op_count:arg2 0.0029577 0.0005341 5.538 0.0000000464 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 734.3 on 581 degrees of freedom
## Multiple R-squared: 0.8739, Adjusted R-squared: 0.8729
## F-statistic: 805.6 on 5 and 581 DF, p-value: < 0.00000000000000022
##
## [1] "GASPRICE" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -682.83 -183.65 -18.41 175.11 760.54
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8560.0681 17.6702 484.44 <0.0000000000000002 ***
## op_count 14.8534 0.9098 16.33 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 266.4 on 570 degrees of freedom
## Multiple R-squared: 0.3186, Adjusted R-squared: 0.3174
## F-statistic: 266.5 on 1 and 570 DF, p-value: < 0.00000000000000022
##
## [1] "RETURNDATASIZE" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -766.05 -212.85 -7.36 192.54 856.87
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8579.658 19.205 446.7 <0.0000000000000002 ***
## op_count 25.946 0.994 26.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 293.3 on 576 degrees of freedom
## Multiple R-squared: 0.5419, Adjusted R-squared: 0.5411
## F-statistic: 681.3 on 1 and 576 DF, p-value: < 0.00000000000000022
##
## [1] "RETURNDATACOPY" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2788.7 -650.6 -217.1 675.9 3955.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21176.2692839 143.4424530 147.629 <0.0000000000000002 ***
## op_count 141.3702343 5.9881535 23.608 <0.0000000000000002 ***
## arg0 -0.0027087 0.0087350 -0.310 0.757
## arg1 -0.0043861 0.0080426 -0.545 0.586
## arg2 -0.0068433 0.0123973 -0.552 0.581
## op_count:arg2 0.0063812 0.0006387 9.991 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 924 on 585 degrees of freedom
## Multiple R-squared: 0.8746, Adjusted R-squared: 0.8735
## F-statistic: 815.8 on 5 and 585 DF, p-value: < 0.00000000000000022
##
## [1] "COINBASE" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -700.99 -168.41 -1.01 182.58 747.01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8576.3522 17.4307 492.02 <0.0000000000000002 ***
## op_count 20.7821 0.9017 23.05 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 265 on 579 degrees of freedom
## Multiple R-squared: 0.4785, Adjusted R-squared: 0.4776
## F-statistic: 531.2 on 1 and 579 DF, p-value: < 0.00000000000000022
##
## [1] "TIMESTAMP" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -761.98 -188.37 -16.33 185.53 774.48
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8550.7711 17.9603 476.09 <0.0000000000000002 ***
## op_count 17.7867 0.9257 19.21 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 274.6 on 582 degrees of freedom
## Multiple R-squared: 0.3881, Adjusted R-squared: 0.3871
## F-statistic: 369.2 on 1 and 582 DF, p-value: < 0.00000000000000022
##
## [1] "NUMBER" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -783.8 -207.8 -15.4 186.5 854.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8608.4780 19.1796 448.84 <0.0000000000000002 ***
## op_count 16.9763 0.9968 17.03 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 293.4 on 579 degrees of freedom
## Multiple R-squared: 0.3338, Adjusted R-squared: 0.3326
## F-statistic: 290 on 1 and 579 DF, p-value: < 0.00000000000000022
##
## [1] "DIFFICULTY" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -779.83 -197.81 6.73 189.19 894.23
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8608.5134 19.1464 449.62 <0.0000000000000002 ***
## op_count 18.8788 0.9953 18.97 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 292.5 on 575 degrees of freedom
## Multiple R-squared: 0.3849, Adjusted R-squared: 0.3838
## F-statistic: 359.8 on 1 and 575 DF, p-value: < 0.00000000000000022
##
## [1] "GASLIMIT" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -924.86 -217.04 -5.95 200.73 895.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8577.3579 18.7152 458.31 <0.0000000000000002 ***
## op_count 17.7924 0.9708 18.33 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 286.8 on 581 degrees of freedom
## Multiple R-squared: 0.3663, Adjusted R-squared: 0.3653
## F-statistic: 335.9 on 1 and 581 DF, p-value: < 0.00000000000000022
##
## [1] "CHAINID" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -820.03 -201.15 -14.81 180.63 923.89
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8580.2429 19.0271 450.95 <0.0000000000000002 ***
## op_count 25.4710 0.9826 25.92 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 291.1 on 583 degrees of freedom
## Multiple R-squared: 0.5355, Adjusted R-squared: 0.5347
## F-statistic: 672 on 1 and 583 DF, p-value: < 0.00000000000000022
##
## [1] "SELFBALANCE" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -785.41 -202.76 -14.76 188.15 936.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8630.4873 19.3474 446.08 <0.0000000000000002 ***
## op_count 41.9077 0.9964 42.06 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 295.9 on 584 degrees of freedom
## Multiple R-squared: 0.7518, Adjusted R-squared: 0.7514
## F-statistic: 1769 on 1 and 584 DF, p-value: < 0.00000000000000022
##
## [1] "POP" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -706.00 -272.75 -42.77 248.68 1090.41
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10101.361 33.429 302.174 < 0.0000000000000002 ***
## op_count 6.528 1.152 5.669 0.0000000225 ***
## arg0 -1.072 1.504 -0.713 0.476
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 343.3 on 591 degrees of freedom
## Multiple R-squared: 0.0523, Adjusted R-squared: 0.04909
## F-statistic: 16.31 on 2 and 591 DF, p-value: 0.0000001278
##
## [1] "MLOAD" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1055.79 -344.06 -90.35 202.78 1891.65
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13104.617470 52.735202 248.498 <0.0000000000000002 ***
## op_count 69.061206 1.729573 39.930 <0.0000000000000002 ***
## arg0 -0.006007 0.004472 -1.343 0.18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 509 on 568 degrees of freedom
## Multiple R-squared: 0.7375, Adjusted R-squared: 0.7366
## F-statistic: 798 on 2 and 568 DF, p-value: < 0.00000000000000022
##
## [1] "MSTORE" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -854.39 -310.79 -96.03 214.71 1280.83
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12157.377330 55.181926 220.314 <0.0000000000000002 ***
## op_count 57.984212 1.504737 38.534 <0.0000000000000002 ***
## arg0 -0.003292 0.003894 -0.845 0.398
## arg1 -0.002749 0.003816 -0.720 0.472
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 445.1 on 583 degrees of freedom
## Multiple R-squared: 0.7183, Adjusted R-squared: 0.7169
## F-statistic: 495.6 on 3 and 583 DF, p-value: < 0.00000000000000022
##
## [1] "MSTORE8" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -831.36 -244.68 -29.59 186.55 998.66
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12093.51369224 42.82232433 282.411 <0.0000000000000002 ***
## op_count 50.61794068 1.23148482 41.103 <0.0000000000000002 ***
## arg0 -0.00006822 0.00325809 -0.021 0.983
## arg1 0.00323321 0.00328781 0.983 0.326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 354.7 on 556 degrees of freedom
## Multiple R-squared: 0.7526, Adjusted R-squared: 0.7513
## F-statistic: 563.8 on 3 and 556 DF, p-value: < 0.00000000000000022
##
## [1] "JUMP" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -829.53 -235.10 -37.89 231.37 1048.52
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8955.623 22.032 406.48 <0.0000000000000002 ***
## op_count 41.160 1.142 36.05 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 337.8 on 583 degrees of freedom
## Multiple R-squared: 0.6903, Adjusted R-squared: 0.6898
## F-statistic: 1299 on 1 and 583 DF, p-value: < 0.00000000000000022
##
## [1] "JUMPI" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2173.1 -1010.4 -322.8 849.4 3848.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12147.537 126.092 96.339 <0.0000000000000002 ***
## op_count 168.109 4.470 37.611 <0.0000000000000002 ***
## arg0 -3.159 5.762 -0.548 0.584
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1334 on 590 degrees of freedom
## Multiple R-squared: 0.7057, Adjusted R-squared: 0.7047
## F-statistic: 707.4 on 2 and 590 DF, p-value: < 0.00000000000000022
##
## [1] "PC" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -714.94 -183.24 -2.02 187.85 856.76
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8613.8005 18.5578 464.16 <0.0000000000000002 ***
## op_count 10.2778 0.9624 10.68 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 284 on 582 degrees of freedom
## Multiple R-squared: 0.1638, Adjusted R-squared: 0.1624
## F-statistic: 114 on 1 and 582 DF, p-value: < 0.00000000000000022
##
## [1] "MSIZE" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -761.8 -214.1 -3.9 188.7 767.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8567.2063 18.9697 451.63 <0.0000000000000002 ***
## op_count 16.7091 0.9789 17.07 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 287.7 on 578 degrees of freedom
## Multiple R-squared: 0.3351, Adjusted R-squared: 0.334
## F-statistic: 291.3 on 1 and 578 DF, p-value: < 0.00000000000000022
##
## [1] "GAS" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -723.70 -185.10 3.86 165.24 676.72
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8598.7271 17.9738 478.40 <0.0000000000000002 ***
## op_count 13.6954 0.9322 14.69 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 273.6 on 576 degrees of freedom
## Multiple R-squared: 0.2726, Adjusted R-squared: 0.2713
## F-statistic: 215.8 on 1 and 576 DF, p-value: < 0.00000000000000022
##
## [1] "JUMPDEST" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -888.78 -286.10 -23.69 281.02 1067.08
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6283.423 24.917 252.18 <0.0000000000000002 ***
## op_count 41.526 1.284 32.35 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 382.2 on 582 degrees of freedom
## Multiple R-squared: 0.6426, Adjusted R-squared: 0.642
## F-statistic: 1047 on 1 and 582 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH1" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -760.18 -189.96 -5.11 164.30 804.01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8621.5455 18.0797 476.86 <0.0000000000000002 ***
## op_count 8.0399 0.9338 8.61 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 274.1 on 576 degrees of freedom
## Multiple R-squared: 0.114, Adjusted R-squared: 0.1125
## F-statistic: 74.13 on 1 and 576 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH2" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -736.57 -190.92 -2.83 179.90 786.61
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8567.0779 18.3388 467.16 <0.0000000000000002 ***
## op_count 19.3257 0.9507 20.33 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 281.3 on 584 degrees of freedom
## Multiple R-squared: 0.4143, Adjusted R-squared: 0.4133
## F-statistic: 413.2 on 1 and 584 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH3" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -742.89 -196.12 4.72 198.38 813.12
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8594.7378 18.4833 465.00 <0.0000000000000002 ***
## op_count 18.0431 0.9489 19.02 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 280.4 on 575 degrees of freedom
## Multiple R-squared: 0.3861, Adjusted R-squared: 0.385
## F-statistic: 361.6 on 1 and 575 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH4" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -720.22 -173.67 6.21 179.39 700.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8548.070 17.726 482.2 <0.0000000000000002 ***
## op_count 18.277 0.923 19.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 270.6 on 577 degrees of freedom
## Multiple R-squared: 0.4046, Adjusted R-squared: 0.4036
## F-statistic: 392.1 on 1 and 577 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH5" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -744.7 -199.2 -7.7 153.5 950.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8583.1344 18.3076 468.83 <0.0000000000000002 ***
## op_count 18.4357 0.9409 19.59 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 279.4 on 584 degrees of freedom
## Multiple R-squared: 0.3966, Adjusted R-squared: 0.3956
## F-statistic: 383.9 on 1 and 584 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH6" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -818.61 -219.23 1.99 176.69 741.43
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8573.3407 19.1679 447.28 <0.0000000000000002 ***
## op_count 18.2073 0.9907 18.38 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 293.1 on 581 degrees of freedom
## Multiple R-squared: 0.3676, Adjusted R-squared: 0.3665
## F-statistic: 337.8 on 1 and 581 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH7" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -736.68 -203.71 -6.98 188.07 766.00
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8607.818 19.218 447.89 <0.0000000000000002 ***
## op_count 18.487 0.996 18.56 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 293.9 on 580 degrees of freedom
## Multiple R-squared: 0.3726, Adjusted R-squared: 0.3716
## F-statistic: 344.5 on 1 and 580 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH8" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -702.89 -183.62 -14.21 175.35 708.86
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8586.7551 18.1484 473.1 <0.0000000000000002 ***
## op_count 18.9039 0.9405 20.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 279 on 586 degrees of freedom
## Multiple R-squared: 0.4081, Adjusted R-squared: 0.407
## F-statistic: 404 on 1 and 586 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH9" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -789.8 -200.1 -9.3 186.3 778.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8551.26 20.10 425.43 <0.0000000000000002 ***
## op_count 21.59 1.04 20.76 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 307.3 on 580 degrees of freedom
## Multiple R-squared: 0.4263, Adjusted R-squared: 0.4253
## F-statistic: 430.9 on 1 and 580 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH10" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -860.79 -221.31 -28.98 196.67 915.77
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8601.141 20.207 425.7 <0.0000000000000002 ***
## op_count 19.106 1.044 18.3 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 309.2 on 578 degrees of freedom
## Multiple R-squared: 0.3669, Adjusted R-squared: 0.3658
## F-statistic: 335 on 1 and 578 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH11" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -839.40 -175.72 -1.14 160.04 901.34
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8575.3982 17.9186 478.57 <0.0000000000000002 ***
## op_count 19.5224 0.9279 21.04 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 272.7 on 576 degrees of freedom
## Multiple R-squared: 0.4346, Adjusted R-squared: 0.4336
## F-statistic: 442.7 on 1 and 576 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH12" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -804.60 -202.69 -2.28 185.75 903.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8604.410 19.302 445.77 <0.0000000000000002 ***
## op_count 17.586 0.998 17.62 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 296 on 585 degrees of freedom
## Multiple R-squared: 0.3468, Adjusted R-squared: 0.3456
## F-statistic: 310.5 on 1 and 585 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH13" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -732.27 -198.83 -12.59 188.19 811.75
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8569.433 19.397 441.8 <0.0000000000000002 ***
## op_count 18.609 1.006 18.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 297.9 on 583 degrees of freedom
## Multiple R-squared: 0.3699, Adjusted R-squared: 0.3689
## F-statistic: 342.3 on 1 and 583 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH14" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -744.16 -184.50 5.08 162.88 774.83
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8588.6404 17.6051 487.9 <0.0000000000000002 ***
## op_count 16.7873 0.9026 18.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 263.6 on 567 degrees of freedom
## Multiple R-squared: 0.3789, Adjusted R-squared: 0.3778
## F-statistic: 345.9 on 1 and 567 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH15" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -781.92 -206.37 -15.83 193.45 873.94
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8574.182 19.412 441.69 <0.0000000000000002 ***
## op_count 16.568 1.001 16.54 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 297.4 on 583 degrees of freedom
## Multiple R-squared: 0.3195, Adjusted R-squared: 0.3183
## F-statistic: 273.7 on 1 and 583 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH16" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -966.47 -211.53 -22.95 214.64 865.05
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8671.858 21.556 402.29 <0.0000000000000002 ***
## op_count 17.383 1.113 15.62 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 328.4 on 581 degrees of freedom
## Multiple R-squared: 0.2957, Adjusted R-squared: 0.2945
## F-statistic: 243.9 on 1 and 581 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH17" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -914.01 -227.28 -12.44 241.57 947.46
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8729.293 21.735 401.6 <0.0000000000000002 ***
## op_count 16.998 1.126 15.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 332.2 on 578 degrees of freedom
## Multiple R-squared: 0.2829, Adjusted R-squared: 0.2817
## F-statistic: 228 on 1 and 578 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH18" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -919.66 -226.60 -21.89 203.51 1057.30
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8715.23 22.65 384.8 <0.0000000000000002 ***
## op_count 16.85 1.17 14.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 345.6 on 581 degrees of freedom
## Multiple R-squared: 0.263, Adjusted R-squared: 0.2618
## F-statistic: 207.4 on 1 and 581 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH19" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -787.07 -231.73 -14.31 219.96 985.68
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8712.301 21.512 405.00 <0.0000000000000002 ***
## op_count 17.702 1.112 15.92 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 327.3 on 576 degrees of freedom
## Multiple R-squared: 0.3055, Adjusted R-squared: 0.3042
## F-statistic: 253.3 on 1 and 576 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH20" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -740.99 -206.60 -8.08 170.42 808.19
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8589.684 18.878 455.00 <0.0000000000000002 ***
## op_count 15.344 0.981 15.64 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 289.1 on 577 degrees of freedom
## Multiple R-squared: 0.2978, Adjusted R-squared: 0.2965
## F-statistic: 244.7 on 1 and 577 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH21" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -848.89 -196.54 -11.57 200.29 898.26
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8588.2554 19.0957 449.75 <0.0000000000000002 ***
## op_count 17.5100 0.9849 17.78 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 290.6 on 578 degrees of freedom
## Multiple R-squared: 0.3535, Adjusted R-squared: 0.3524
## F-statistic: 316.1 on 1 and 578 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH22" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -727.06 -205.82 -4.57 176.30 717.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8597.7711 18.3662 468.13 <0.0000000000000002 ***
## op_count 15.6734 0.9473 16.55 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 280.3 on 581 degrees of freedom
## Multiple R-squared: 0.3203, Adjusted R-squared: 0.3191
## F-statistic: 273.8 on 1 and 581 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH23" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -824.86 -199.69 -17.85 196.23 818.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8571.614 19.723 434.61 <0.0000000000000002 ***
## op_count 17.500 1.015 17.25 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 300.6 on 584 degrees of freedom
## Multiple R-squared: 0.3375, Adjusted R-squared: 0.3363
## F-statistic: 297.5 on 1 and 584 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH24" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -807.09 -182.55 -17.22 178.50 796.67
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8564.7155 18.0039 475.71 <0.0000000000000002 ***
## op_count 18.9060 0.9302 20.32 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 275.2 on 580 degrees of freedom
## Multiple R-squared: 0.416, Adjusted R-squared: 0.415
## F-statistic: 413.1 on 1 and 580 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH25" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -685.9 -190.3 -4.4 179.8 759.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8615.6263 18.4838 466.12 <0.0000000000000002 ***
## op_count 15.1888 0.9535 15.93 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 283.2 on 583 degrees of freedom
## Multiple R-squared: 0.3032, Adjusted R-squared: 0.3021
## F-statistic: 253.7 on 1 and 583 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH26" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -735.61 -228.23 -25.25 210.97 951.19
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8602.794 20.790 413.79 <0.0000000000000002 ***
## op_count 16.416 1.074 15.28 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 318.2 on 585 degrees of freedom
## Multiple R-squared: 0.2853, Adjusted R-squared: 0.2841
## F-statistic: 233.5 on 1 and 585 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH27" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -738.13 -222.93 -28.73 219.68 831.22
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8571.381 19.743 434.16 <0.0000000000000002 ***
## op_count 16.496 1.019 16.19 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 302.1 on 585 degrees of freedom
## Multiple R-squared: 0.3095, Adjusted R-squared: 0.3083
## F-statistic: 262.2 on 1 and 585 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH28" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -786.15 -224.20 -14.59 196.75 877.41
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8596.844 20.337 422.72 <0.0000000000000002 ***
## op_count 14.241 1.048 13.59 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 309.2 on 581 degrees of freedom
## Multiple R-squared: 0.2412, Adjusted R-squared: 0.2399
## F-statistic: 184.7 on 1 and 581 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH29" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -821.77 -197.36 -8.19 203.43 893.98
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8622.589 19.602 439.89 <0.0000000000000002 ***
## op_count 13.732 1.019 13.48 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 300.6 on 582 degrees of freedom
## Multiple R-squared: 0.2379, Adjusted R-squared: 0.2366
## F-statistic: 181.7 on 1 and 582 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH30" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -696.08 -195.04 -13.31 192.26 932.64
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8621.4474 18.9917 453.96 <0.0000000000000002 ***
## op_count 13.0241 0.9787 13.31 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 290.7 on 586 degrees of freedom
## Multiple R-squared: 0.2321, Adjusted R-squared: 0.2307
## F-statistic: 177.1 on 1 and 586 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH31" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -780.85 -211.14 -0.23 195.06 934.35
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8618.609 19.554 440.76 <0.0000000000000002 ***
## op_count 14.705 1.005 14.63 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 297.2 on 582 degrees of freedom
## Multiple R-squared: 0.269, Adjusted R-squared: 0.2677
## F-statistic: 214.2 on 1 and 582 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH32" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -834.58 -196.37 -11.14 185.44 849.52
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8607.6226 19.1220 450.14 <0.0000000000000002 ***
## op_count 11.2099 0.9878 11.35 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 292.6 on 584 degrees of freedom
## Multiple R-squared: 0.1807, Adjusted R-squared: 0.1793
## F-statistic: 128.8 on 1 and 584 DF, p-value: < 0.00000000000000022
##
## [1] "DUP1" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -904.55 -291.17 -78.16 287.97 1243.54
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11269.2199 39.0196 288.809 < 0.0000000000000002 ***
## op_count 10.5183 1.3124 8.015 0.00000000000000604 ***
## arg0 -0.7194 1.7478 -0.412 0.681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 389.8 on 584 degrees of freedom
## Multiple R-squared: 0.09927, Adjusted R-squared: 0.09619
## F-statistic: 32.18 on 2 and 584 DF, p-value: 0.00000000000005509
##
## [1] "DUP2" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -967.73 -310.13 -52.55 314.06 1337.91
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11262.8770 38.0726 295.827 < 0.0000000000000002 ***
## op_count 10.8506 1.3480 8.050 0.00000000000000456 ***
## arg0 0.8271 1.7135 0.483 0.629
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 403.4 on 593 degrees of freedom
## Multiple R-squared: 0.09886, Adjusted R-squared: 0.09582
## F-statistic: 32.53 on 2 and 593 DF, p-value: 0.00000000000003939
##
## [1] "DUP3" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1130.10 -323.99 -50.49 334.12 1217.48
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11338.644 39.450 287.422 < 0.0000000000000002 ***
## op_count 9.482 1.340 7.074 0.00000000000431 ***
## arg0 -3.419 1.846 -1.852 0.0645 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 398.5 on 587 degrees of freedom
## Multiple R-squared: 0.0841, Adjusted R-squared: 0.08098
## F-statistic: 26.95 on 2 and 587 DF, p-value: 0.000000000006343
##
## [1] "DUP4" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -973.28 -295.11 -34.31 309.26 1263.99
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11228.066 37.774 297.247 <0.0000000000000002 ***
## op_count 11.064 1.302 8.497 <0.0000000000000002 ***
## arg0 1.576 1.704 0.925 0.355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 388.7 on 589 degrees of freedom
## Multiple R-squared: 0.1103, Adjusted R-squared: 0.1072
## F-statistic: 36.5 on 2 and 589 DF, p-value: 0.00000000000000114
##
## [1] "DUP5" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -990.50 -311.58 -58.51 325.82 1368.32
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11288.0627 39.0529 289.046 < 0.0000000000000002 ***
## op_count 10.4295 1.3663 7.633 0.0000000000000936 ***
## arg0 -0.2588 1.7371 -0.149 0.882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 405.3 on 585 degrees of freedom
## Multiple R-squared: 0.0906, Adjusted R-squared: 0.08749
## F-statistic: 29.14 on 2 and 585 DF, p-value: 0.000000000000863
##
## [1] "DUP6" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -940.58 -367.30 -40.53 334.94 1444.33
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11202.520 44.875 249.637 < 0.0000000000000002 ***
## op_count 11.153 1.467 7.605 0.000000000000113 ***
## arg0 3.861 2.027 1.904 0.0573 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 438.3 on 592 degrees of freedom
## Multiple R-squared: 0.09393, Adjusted R-squared: 0.09087
## F-statistic: 30.69 on 2 and 592 DF, p-value: 0.0000000000002089
##
## [1] "DUP7" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1180.40 -327.36 -33.56 308.45 1321.45
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11327.659 42.647 265.612 < 0.0000000000000002 ***
## op_count 10.325 1.435 7.193 0.00000000000195 ***
## arg0 1.543 1.970 0.783 0.434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 426.3 on 585 degrees of freedom
## Multiple R-squared: 0.0821, Adjusted R-squared: 0.07897
## F-statistic: 26.16 on 2 and 585 DF, p-value: 0.0000000000131
##
## [1] "DUP8" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -958.79 -317.58 -46.39 338.34 1341.96
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11259.113 39.381 285.905 <0.0000000000000002 ***
## op_count 11.884 1.382 8.598 <0.0000000000000002 ***
## arg0 4.169 1.902 2.192 0.0288 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 409.4 on 583 degrees of freedom
## Multiple R-squared: 0.1191, Adjusted R-squared: 0.1161
## F-statistic: 39.4 on 2 and 583 DF, p-value: < 0.00000000000000022
##
## [1] "DUP9" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -966.57 -350.47 -25.69 332.88 1237.90
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11283.186 42.637 264.632 <0.0000000000000002 ***
## op_count 12.315 1.447 8.510 <0.0000000000000002 ***
## arg0 1.042 1.987 0.524 0.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 428.1 on 584 degrees of freedom
## Multiple R-squared: 0.1107, Adjusted R-squared: 0.1076
## F-statistic: 36.34 on 2 and 584 DF, p-value: 0.000000000000001333
##
## [1] "DUP10" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -935.05 -306.12 -32.03 328.39 1077.77
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11259.7377 40.4552 278.326 <0.0000000000000002 ***
## op_count 12.2482 1.3563 9.030 <0.0000000000000002 ***
## arg0 0.7413 1.7385 0.426 0.67
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 402.3 on 583 degrees of freedom
## Multiple R-squared: 0.1229, Adjusted R-squared: 0.1199
## F-statistic: 40.86 on 2 and 583 DF, p-value: < 0.00000000000000022
##
## [1] "DUP11" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -913.36 -357.20 -77.92 346.75 1547.33
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11417.218 45.434 251.291 < 0.0000000000000002 ***
## op_count 10.841 1.518 7.142 0.00000000000273 ***
## arg0 -1.772 2.047 -0.866 0.387
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 450.8 on 588 degrees of freedom
## Multiple R-squared: 0.08104, Adjusted R-squared: 0.07792
## F-statistic: 25.93 on 2 and 588 DF, p-value: 0.00000000001617
##
## [1] "DUP12" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -969.03 -312.80 -81.79 307.36 1208.82
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11289.313 39.365 286.783 < 0.0000000000000002 ***
## op_count 9.755 1.371 7.114 0.0000000000033 ***
## arg0 -1.516 1.780 -0.852 0.395
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 408.2 on 585 degrees of freedom
## Multiple R-squared: 0.08061, Adjusted R-squared: 0.07747
## F-statistic: 25.65 on 2 and 585 DF, p-value: 0.00000000002106
##
## [1] "DUP13" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -885.14 -320.69 -64.74 314.82 1229.80
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11282.997 43.029 262.215 <0.0000000000000002 ***
## op_count 12.079 1.422 8.496 <0.0000000000000002 ***
## arg0 0.699 1.923 0.364 0.716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 423.8 on 588 degrees of freedom
## Multiple R-squared: 0.1095, Adjusted R-squared: 0.1064
## F-statistic: 36.14 on 2 and 588 DF, p-value: 0.000000000000001576
##
## [1] "DUP14" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -956.68 -295.60 -76.71 285.42 1135.30
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11241.972 38.441 292.450 < 0.0000000000000002 ***
## op_count 10.878 1.319 8.249 0.00000000000000105 ***
## arg0 2.403 1.774 1.354 0.176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 393.6 on 587 degrees of freedom
## Multiple R-squared: 0.1063, Adjusted R-squared: 0.1033
## F-statistic: 34.92 on 2 and 587 DF, p-value: 0.0000000000000047
##
## [1] "DUP15" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -979.79 -327.67 -82.56 308.39 1482.68
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11229.721 43.758 256.633 < 0.0000000000000002 ***
## op_count 9.215 1.391 6.622 0.0000000000796 ***
## arg0 1.647 1.990 0.828 0.408
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 415.9 on 592 degrees of freedom
## Multiple R-squared: 0.07001, Adjusted R-squared: 0.06687
## F-statistic: 22.28 on 2 and 592 DF, p-value: 0.000000000467
##
## [1] "DUP16" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -947.29 -343.57 -87.61 322.18 1419.25
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11429.3029 42.3430 269.922 < 0.0000000000000002 ***
## op_count 11.8094 1.4341 8.235 0.00000000000000118 ***
## arg0 0.7962 1.9049 0.418 0.676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 425.4 on 584 degrees of freedom
## Multiple R-squared: 0.1042, Adjusted R-squared: 0.1012
## F-statistic: 33.98 on 2 and 584 DF, p-value: 0.00000000000001098
##
## [1] "SWAP1" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -754.60 -267.08 -19.53 249.10 994.76
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10119.1725 42.2892 239.285 <0.0000000000000002 ***
## op_count 11.9207 1.1579 10.295 <0.0000000000000002 ***
## arg0 0.8334 1.5545 0.536 0.592
## arg1 -0.4021 1.5130 -0.266 0.791
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 345.2 on 587 degrees of freedom
## Multiple R-squared: 0.1534, Adjusted R-squared: 0.1491
## F-statistic: 35.46 on 3 and 587 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP2" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -985.9 -268.2 -46.4 266.0 1180.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10126.5937 44.7158 226.466 <0.0000000000000002 ***
## op_count 12.8478 1.2008 10.699 <0.0000000000000002 ***
## arg0 -1.7233 1.6628 -1.036 0.300
## arg1 -0.2961 1.5918 -0.186 0.853
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 357.5 on 584 degrees of freedom
## Multiple R-squared: 0.1652, Adjusted R-squared: 0.1609
## F-statistic: 38.52 on 3 and 584 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP3" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -897.63 -246.43 -29.34 244.33 982.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10178.039 40.857 249.111 <0.0000000000000002 ***
## op_count 12.739 1.139 11.186 <0.0000000000000002 ***
## arg0 -1.576 1.506 -1.046 0.296
## arg1 -1.912 1.461 -1.308 0.191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 336.9 on 583 degrees of freedom
## Multiple R-squared: 0.1792, Adjusted R-squared: 0.1749
## F-statistic: 42.41 on 3 and 583 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP4" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -935.11 -252.39 -49.44 243.34 998.56
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10057.229 42.445 236.947 <0.0000000000000002 ***
## op_count 12.749 1.140 11.182 <0.0000000000000002 ***
## arg0 -1.559 1.584 -0.984 0.3253
## arg1 3.030 1.512 2.004 0.0455 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 339 on 582 degrees of freedom
## Multiple R-squared: 0.1822, Adjusted R-squared: 0.178
## F-statistic: 43.22 on 3 and 582 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP5" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1003.84 -255.25 -46.51 260.39 996.12
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10126.2660 45.6735 221.710 <0.0000000000000002 ***
## op_count 11.9691 1.1787 10.154 <0.0000000000000002 ***
## arg0 0.3563 1.5632 0.228 0.820
## arg1 -1.5470 1.6845 -0.918 0.359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 350 on 585 degrees of freedom
## Multiple R-squared: 0.1512, Adjusted R-squared: 0.1469
## F-statistic: 34.74 on 3 and 585 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP6" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -869.02 -246.08 -44.88 235.45 1030.51
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10064.014 43.786 229.844 <0.0000000000000002 ***
## op_count 12.409 1.144 10.846 <0.0000000000000002 ***
## arg0 2.000 1.531 1.306 0.192
## arg1 2.276 1.519 1.499 0.134
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 340.6 on 589 degrees of freedom
## Multiple R-squared: 0.1708, Adjusted R-squared: 0.1666
## F-statistic: 40.45 on 3 and 589 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP7" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -824.77 -252.35 -40.45 231.21 1012.95
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10104.3488 44.2203 228.500 < 0.0000000000000002 ***
## op_count 12.9220 1.1708 11.037 < 0.0000000000000002 ***
## arg0 -0.7647 1.6217 -0.472 0.63742
## arg1 4.8788 1.6667 2.927 0.00355 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 346.8 on 584 degrees of freedom
## Multiple R-squared: 0.1818, Adjusted R-squared: 0.1776
## F-statistic: 43.26 on 3 and 584 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP8" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -810.19 -267.25 -22.93 270.70 1085.00
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10068.2050 41.7488 241.161 <0.0000000000000002 ***
## op_count 14.6038 1.1752 12.427 <0.0000000000000002 ***
## arg0 0.8341 1.5727 0.530 0.596
## arg1 0.5642 1.6035 0.352 0.725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 350.3 on 585 degrees of freedom
## Multiple R-squared: 0.2093, Adjusted R-squared: 0.2052
## F-statistic: 51.62 on 3 and 585 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP9" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1027.25 -255.86 -27.35 288.04 1092.56
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10117.7156 47.7695 211.803 <0.0000000000000002 ***
## op_count 13.6676 1.2168 11.232 <0.0000000000000002 ***
## arg0 -0.3033 1.7212 -0.176 0.860
## arg1 -0.7178 1.6270 -0.441 0.659
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 362.7 on 589 degrees of freedom
## Multiple R-squared: 0.1767, Adjusted R-squared: 0.1725
## F-statistic: 42.14 on 3 and 589 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP10" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -801.78 -283.50 -48.87 282.96 1095.58
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10222.1139 46.2483 221.027 <0.0000000000000002 ***
## op_count 14.6883 1.1981 12.260 <0.0000000000000002 ***
## arg0 -0.2537 1.5544 -0.163 0.870
## arg1 -0.6171 1.6901 -0.365 0.715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 357.2 on 587 degrees of freedom
## Multiple R-squared: 0.204, Adjusted R-squared: 0.2
## F-statistic: 50.15 on 3 and 587 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP11" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -692.62 -276.48 -55.97 266.26 1102.46
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10052.9470 43.1044 233.223 <0.0000000000000002 ***
## op_count 13.7845 1.2031 11.458 <0.0000000000000002 ***
## arg0 1.6605 1.6557 1.003 0.316
## arg1 0.3939 1.6259 0.242 0.809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 358.2 on 587 degrees of freedom
## Multiple R-squared: 0.1842, Adjusted R-squared: 0.18
## F-statistic: 44.17 on 3 and 587 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP12" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -931.40 -278.83 -52.16 268.33 1104.50
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10140.769 42.946 236.126 <0.0000000000000002 ***
## op_count 14.361 1.232 11.661 <0.0000000000000002 ***
## arg0 1.620 1.590 1.019 0.309
## arg1 -1.879 1.632 -1.152 0.250
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 365.7 on 585 degrees of freedom
## Multiple R-squared: 0.1905, Adjusted R-squared: 0.1864
## F-statistic: 45.9 on 3 and 585 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP13" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -756.23 -264.31 -26.06 255.00 1068.64
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10156.2557 41.0362 247.495 <0.0000000000000002 ***
## op_count 11.7226 1.1751 9.976 <0.0000000000000002 ***
## arg0 -0.7732 1.5555 -0.497 0.619
## arg1 1.3540 1.5397 0.879 0.380
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 347.6 on 583 degrees of freedom
## Multiple R-squared: 0.1469, Adjusted R-squared: 0.1425
## F-statistic: 33.46 on 3 and 583 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP14" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1046.71 -285.80 -37.67 268.08 1192.07
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10088.79686 44.27695 227.857 <0.0000000000000002 ***
## op_count 12.31666 1.23850 9.945 <0.0000000000000002 ***
## arg0 1.94719 1.73644 1.121 0.263
## arg1 -0.03987 1.64756 -0.024 0.981
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 370.1 on 589 degrees of freedom
## Multiple R-squared: 0.1454, Adjusted R-squared: 0.141
## F-statistic: 33.39 on 3 and 589 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP15" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -748.78 -283.34 -51.76 266.59 1119.72
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10343.6548 46.0831 224.457 <0.0000000000000002 ***
## op_count 11.7495 1.2272 9.574 <0.0000000000000002 ***
## arg0 -0.1859 1.6403 -0.113 0.9098
## arg1 -2.7067 1.6395 -1.651 0.0993 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 366.3 on 586 degrees of freedom
## Multiple R-squared: 0.1387, Adjusted R-squared: 0.1342
## F-statistic: 31.44 on 3 and 586 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP16" "nethermind"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -909.03 -276.99 -36.49 263.15 1062.03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10193.81279 45.22262 225.414 <0.0000000000000002 ***
## op_count 13.01123 1.23277 10.554 <0.0000000000000002 ***
## arg0 0.07923 1.57255 0.050 0.960
## arg1 1.59828 1.69744 0.942 0.347
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 367.5 on 590 degrees of freedom
## Multiple R-squared: 0.1598, Adjusted R-squared: 0.1556
## F-statistic: 37.41 on 3 and 590 DF, p-value: < 0.00000000000000022estimates## opcode env has_significant has_impacting
## 1 ADD nethermind FALSE FALSE
## 2 MUL nethermind FALSE FALSE
## 3 SUB nethermind FALSE FALSE
## 4 DIV nethermind TRUE TRUE
## 5 SDIV nethermind TRUE TRUE
## 6 MOD nethermind TRUE TRUE
## 7 SMOD nethermind TRUE TRUE
## 8 ADDMOD nethermind TRUE TRUE
## 9 MULMOD nethermind TRUE TRUE
## 10 EXP nethermind TRUE TRUE
## 11 SIGNEXTEND nethermind FALSE FALSE
## 12 LT nethermind FALSE FALSE
## 13 GT nethermind FALSE FALSE
## 14 SLT nethermind FALSE FALSE
## 15 SGT nethermind FALSE FALSE
## 16 EQ nethermind FALSE FALSE
## 17 ISZERO nethermind FALSE FALSE
## 18 AND nethermind FALSE FALSE
## 19 OR nethermind FALSE FALSE
## 20 XOR nethermind FALSE FALSE
## 21 NOT nethermind FALSE FALSE
## 22 BYTE nethermind FALSE FALSE
## 23 SHL nethermind FALSE FALSE
## 24 SHR nethermind FALSE FALSE
## 25 SAR nethermind FALSE FALSE
## 26 ADDRESS nethermind FALSE FALSE
## 27 ORIGIN nethermind FALSE FALSE
## 28 CALLER nethermind FALSE FALSE
## 29 CALLVALUE nethermind FALSE FALSE
## 30 CALLDATALOAD nethermind FALSE FALSE
## 31 CALLDATASIZE nethermind FALSE FALSE
## 32 CALLDATACOPY nethermind TRUE TRUE
## 33 CODESIZE nethermind FALSE FALSE
## 34 CODECOPY nethermind TRUE TRUE
## 35 GASPRICE nethermind FALSE FALSE
## 36 RETURNDATASIZE nethermind FALSE FALSE
## 37 RETURNDATACOPY nethermind TRUE TRUE
## 38 COINBASE nethermind FALSE FALSE
## 39 TIMESTAMP nethermind FALSE FALSE
## 40 NUMBER nethermind FALSE FALSE
## 41 DIFFICULTY nethermind FALSE FALSE
## 42 GASLIMIT nethermind FALSE FALSE
## 43 CHAINID nethermind FALSE FALSE
## 44 SELFBALANCE nethermind FALSE FALSE
## 45 POP nethermind FALSE FALSE
## 46 MLOAD nethermind FALSE FALSE
## 47 MSTORE nethermind FALSE FALSE
## 48 MSTORE8 nethermind FALSE FALSE
## 49 JUMP nethermind FALSE FALSE
## 50 JUMPI nethermind FALSE FALSE
## 51 PC nethermind FALSE FALSE
## 52 MSIZE nethermind FALSE FALSE
## 53 GAS nethermind FALSE FALSE
## 54 JUMPDEST nethermind FALSE FALSE
## 55 PUSH1 nethermind FALSE FALSE
## 56 PUSH2 nethermind FALSE FALSE
## 57 PUSH3 nethermind FALSE FALSE
## 58 PUSH4 nethermind FALSE FALSE
## 59 PUSH5 nethermind FALSE FALSE
## 60 PUSH6 nethermind FALSE FALSE
## 61 PUSH7 nethermind FALSE FALSE
## 62 PUSH8 nethermind FALSE FALSE
## 63 PUSH9 nethermind FALSE FALSE
## 64 PUSH10 nethermind FALSE FALSE
## 65 PUSH11 nethermind FALSE FALSE
## 66 PUSH12 nethermind FALSE FALSE
## 67 PUSH13 nethermind FALSE FALSE
## 68 PUSH14 nethermind FALSE FALSE
## 69 PUSH15 nethermind FALSE FALSE
## 70 PUSH16 nethermind FALSE FALSE
## 71 PUSH17 nethermind FALSE FALSE
## 72 PUSH18 nethermind FALSE FALSE
## 73 PUSH19 nethermind FALSE FALSE
## 74 PUSH20 nethermind FALSE FALSE
## 75 PUSH21 nethermind FALSE FALSE
## 76 PUSH22 nethermind FALSE FALSE
## 77 PUSH23 nethermind FALSE FALSE
## 78 PUSH24 nethermind FALSE FALSE
## 79 PUSH25 nethermind FALSE FALSE
## 80 PUSH26 nethermind FALSE FALSE
## 81 PUSH27 nethermind FALSE FALSE
## 82 PUSH28 nethermind FALSE FALSE
## 83 PUSH29 nethermind FALSE FALSE
## 84 PUSH30 nethermind FALSE FALSE
## 85 PUSH31 nethermind FALSE FALSE
## 86 PUSH32 nethermind FALSE FALSE
## 87 DUP1 nethermind FALSE FALSE
## 88 DUP2 nethermind FALSE FALSE
## 89 DUP3 nethermind FALSE FALSE
## 90 DUP4 nethermind FALSE FALSE
## 91 DUP5 nethermind FALSE FALSE
## 92 DUP6 nethermind FALSE FALSE
## 93 DUP7 nethermind FALSE FALSE
## 94 DUP8 nethermind FALSE FALSE
## 95 DUP9 nethermind FALSE FALSE
## 96 DUP10 nethermind FALSE FALSE
## 97 DUP11 nethermind FALSE FALSE
## 98 DUP12 nethermind FALSE FALSE
## 99 DUP13 nethermind FALSE FALSE
## 100 DUP14 nethermind FALSE FALSE
## 101 DUP15 nethermind FALSE FALSE
## 102 DUP16 nethermind FALSE FALSE
## 103 SWAP1 nethermind FALSE FALSE
## 104 SWAP2 nethermind FALSE FALSE
## 105 SWAP3 nethermind FALSE FALSE
## 106 SWAP4 nethermind FALSE FALSE
## 107 SWAP5 nethermind FALSE FALSE
## 108 SWAP6 nethermind FALSE FALSE
## 109 SWAP7 nethermind FALSE FALSE
## 110 SWAP8 nethermind FALSE FALSE
## 111 SWAP9 nethermind FALSE FALSE
## 112 SWAP10 nethermind FALSE FALSE
## 113 SWAP11 nethermind FALSE FALSE
## 114 SWAP12 nethermind FALSE FALSE
## 115 SWAP13 nethermind FALSE FALSE
## 116 SWAP14 nethermind FALSE FALSE
## 117 SWAP15 nethermind FALSE FALSE
## 118 SWAP16 nethermind FALSE FALSE
## estimate_marginal_ns arg0_ns arg1_ns arg2_ns
## 1 31.4480362646858 <NA> <NA> <NA>
## 2 61.7786313605039 <NA> <NA> <NA>
## 3 31.5026898922074 <NA> <NA> <NA>
## 4 29.7124999719411 <NA> <NA> <NA>
## 5 44.3956105761923 <NA> <NA> <NA>
## 6 29.1611483326222 <NA> <NA> <NA>
## 7 52.3264911399163 <NA> <NA> <NA>
## 8 54.7506374499178 <NA> <NA> <NA>
## 9 137.088751995398 <NA> <NA> <NA>
## 10 858.792164462129 <NA> 274.257023617893 <NA>
## 11 10.2144122709573 <NA> <NA> <NA>
## 12 21.2865489397508 <NA> <NA> <NA>
## 13 23.2609624999515 <NA> <NA> <NA>
## 14 34.8653453181971 <NA> <NA> <NA>
## 15 27.7275021004007 <NA> <NA> <NA>
## 16 20.82583406751 <NA> <NA> <NA>
## 17 20.2340034179102 <NA> <NA> <NA>
## 18 42.7192381656865 <NA> <NA> <NA>
## 19 43.1458898244736 <NA> <NA> <NA>
## 20 42.7257830685645 <NA> <NA> <NA>
## 21 32.3576977650141 <NA> <NA> <NA>
## 22 20.9830023696138 <NA> <NA> <NA>
## 23 27.6430586234422 <NA> <NA> <NA>
## 24 26.4994703718385 <NA> <NA> <NA>
## 25 37.8913563341062 <NA> <NA> <NA>
## 26 19.2977539314556 <NA> <NA> <NA>
## 27 20.1084398101392 <NA> <NA> <NA>
## 28 18.5808263990613 <NA> <NA> <NA>
## 29 10.1892655582266 <NA> <NA> <NA>
## 30 28.2003357691992 <NA> <NA> <NA>
## 31 16.8559203684234 <NA> <NA> <NA>
## 32 124.410171931004 <NA> <NA> 0.00329064668859508
## 33 15.462669442162 <NA> <NA> <NA>
## 34 131.638438321262 <NA> <NA> 0.00295773948264731
## 35 14.8534316857784 <NA> <NA> <NA>
## 36 25.9456016353638 <NA> <NA> <NA>
## 37 141.370234297858 <NA> <NA> 0.00638116257436516
## 38 20.7821182486622 <NA> <NA> <NA>
## 39 17.7867015233196 <NA> <NA> <NA>
## 40 16.9762809632619 <NA> <NA> <NA>
## 41 18.8788364744263 <NA> <NA> <NA>
## 42 17.792435360199 <NA> <NA> <NA>
## 43 25.4710316247015 <NA> <NA> <NA>
## 44 41.907693396385 <NA> <NA> <NA>
## 45 6.52765575600859 <NA> <NA> <NA>
## 46 69.0612062213677 <NA> <NA> <NA>
## 47 57.9842117375483 <NA> <NA> <NA>
## 48 50.6179406815128 <NA> <NA> <NA>
## 49 41.1601814463512 <NA> <NA> <NA>
## 50 168.109447769624 <NA> <NA> <NA>
## 51 10.2778063750932 <NA> <NA> <NA>
## 52 16.7090746617699 <NA> <NA> <NA>
## 53 13.6953560581785 <NA> <NA> <NA>
## 54 41.5263917618967 <NA> <NA> <NA>
## 55 8.03992755484684 <NA> <NA> <NA>
## 56 19.3256695159143 <NA> <NA> <NA>
## 57 18.0430724888324 <NA> <NA> <NA>
## 58 18.2772378239647 <NA> <NA> <NA>
## 59 18.4356589478897 <NA> <NA> <NA>
## 60 18.2072778597174 <NA> <NA> <NA>
## 61 18.487075091643 <NA> <NA> <NA>
## 62 18.9039108858216 <NA> <NA> <NA>
## 63 21.5911280981112 <NA> <NA> <NA>
## 64 19.1062505880463 <NA> <NA> <NA>
## 65 19.5224052605334 <NA> <NA> <NA>
## 66 17.5859076541569 <NA> <NA> <NA>
## 67 18.6086768661342 <NA> <NA> <NA>
## 68 16.7873407719655 <NA> <NA> <NA>
## 69 16.5675658603927 <NA> <NA> <NA>
## 70 17.3829361893053 <NA> <NA> <NA>
## 71 16.9980110756702 <NA> <NA> <NA>
## 72 16.8461135926543 <NA> <NA> <NA>
## 73 17.7021791171376 <NA> <NA> <NA>
## 74 15.3443757018529 <NA> <NA> <NA>
## 75 17.5100369816357 <NA> <NA> <NA>
## 76 15.6733544121339 <NA> <NA> <NA>
## 77 17.5003529342986 <NA> <NA> <NA>
## 78 18.9059740244977 <NA> <NA> <NA>
## 79 15.1888076813989 <NA> <NA> <NA>
## 80 16.4163693785316 <NA> <NA> <NA>
## 81 16.4956947849421 <NA> <NA> <NA>
## 82 14.2407749334242 <NA> <NA> <NA>
## 83 13.7316783440816 <NA> <NA> <NA>
## 84 13.0240528199439 <NA> <NA> <NA>
## 85 14.7046741927553 <NA> <NA> <NA>
## 86 11.2098918666509 <NA> <NA> <NA>
## 87 10.5183258337575 <NA> <NA> <NA>
## 88 10.8506016826713 <NA> <NA> <NA>
## 89 9.48212389043194 <NA> <NA> <NA>
## 90 11.0640348694298 <NA> <NA> <NA>
## 91 10.4294710456491 <NA> <NA> <NA>
## 92 11.1534180300148 <NA> <NA> <NA>
## 93 10.3249134216255 <NA> <NA> <NA>
## 94 11.8838546341636 <NA> <NA> <NA>
## 95 12.3145998430019 <NA> <NA> <NA>
## 96 12.2481878785853 <NA> <NA> <NA>
## 97 10.8406425368947 <NA> <NA> <NA>
## 98 9.75458526094098 <NA> <NA> <NA>
## 99 12.0789679330047 <NA> <NA> <NA>
## 100 10.8776744242078 <NA> <NA> <NA>
## 101 9.21454867853761 <NA> <NA> <NA>
## 102 11.8093848915208 <NA> <NA> <NA>
## 103 11.9206710287547 <NA> <NA> <NA>
## 104 12.8478090965134 <NA> <NA> <NA>
## 105 12.739001083069 <NA> <NA> <NA>
## 106 12.7492154624927 <NA> <NA> <NA>
## 107 11.9690990535591 <NA> <NA> <NA>
## 108 12.4086487099253 <NA> <NA> <NA>
## 109 12.9219526656711 <NA> <NA> <NA>
## 110 14.6037676623349 <NA> <NA> <NA>
## 111 13.6675926650132 <NA> <NA> <NA>
## 112 14.6883246175182 <NA> <NA> <NA>
## 113 13.7845430656563 <NA> <NA> <NA>
## 114 14.3613399049957 <NA> <NA> <NA>
## 115 11.7225517287966 <NA> <NA> <NA>
## 116 12.3166597985577 <NA> <NA> <NA>
## 117 11.7495172007354 <NA> <NA> <NA>
## 118 13.011227097562 <NA> <NA> <NA>
## expensive_ns arg0_ns_stderr arg1_ns_stderr arg2_ns_stderr
## 1 <NA> <NA> <NA> <NA>
## 2 <NA> <NA> <NA> <NA>
## 3 <NA> <NA> <NA> <NA>
## 4 35.4153981950679 <NA> <NA> <NA>
## 5 43.1527202060663 <NA> <NA> <NA>
## 6 36.3036859678608 <NA> <NA> <NA>
## 7 39.267949590233 <NA> <NA> <NA>
## 8 38.586740587816 <NA> <NA> <NA>
## 9 51.3827768858027 <NA> <NA> <NA>
## 10 <NA> <NA> 10.8835460764743 <NA>
## 11 <NA> <NA> <NA> <NA>
## 12 <NA> <NA> <NA> <NA>
## 13 <NA> <NA> <NA> <NA>
## 14 <NA> <NA> <NA> <NA>
## 15 <NA> <NA> <NA> <NA>
## 16 <NA> <NA> <NA> <NA>
## 17 <NA> <NA> <NA> <NA>
## 18 <NA> <NA> <NA> <NA>
## 19 <NA> <NA> <NA> <NA>
## 20 <NA> <NA> <NA> <NA>
## 21 <NA> <NA> <NA> <NA>
## 22 <NA> <NA> <NA> <NA>
## 23 <NA> <NA> <NA> <NA>
## 24 <NA> <NA> <NA> <NA>
## 25 <NA> <NA> <NA> <NA>
## 26 <NA> <NA> <NA> <NA>
## 27 <NA> <NA> <NA> <NA>
## 28 <NA> <NA> <NA> <NA>
## 29 <NA> <NA> <NA> <NA>
## 30 <NA> <NA> <NA> <NA>
## 31 <NA> <NA> <NA> <NA>
## 32 <NA> <NA> <NA> 0.000389164756361538
## 33 <NA> <NA> <NA> <NA>
## 34 <NA> <NA> <NA> 0.000534067604123492
## 35 <NA> <NA> <NA> <NA>
## 36 <NA> <NA> <NA> <NA>
## 37 <NA> <NA> <NA> 0.000638714897400409
## 38 <NA> <NA> <NA> <NA>
## 39 <NA> <NA> <NA> <NA>
## 40 <NA> <NA> <NA> <NA>
## 41 <NA> <NA> <NA> <NA>
## 42 <NA> <NA> <NA> <NA>
## 43 <NA> <NA> <NA> <NA>
## 44 <NA> <NA> <NA> <NA>
## 45 <NA> <NA> <NA> <NA>
## 46 <NA> <NA> <NA> <NA>
## 47 <NA> <NA> <NA> <NA>
## 48 <NA> <NA> <NA> <NA>
## 49 <NA> <NA> <NA> <NA>
## 50 <NA> <NA> <NA> <NA>
## 51 <NA> <NA> <NA> <NA>
## 52 <NA> <NA> <NA> <NA>
## 53 <NA> <NA> <NA> <NA>
## 54 <NA> <NA> <NA> <NA>
## 55 <NA> <NA> <NA> <NA>
## 56 <NA> <NA> <NA> <NA>
## 57 <NA> <NA> <NA> <NA>
## 58 <NA> <NA> <NA> <NA>
## 59 <NA> <NA> <NA> <NA>
## 60 <NA> <NA> <NA> <NA>
## 61 <NA> <NA> <NA> <NA>
## 62 <NA> <NA> <NA> <NA>
## 63 <NA> <NA> <NA> <NA>
## 64 <NA> <NA> <NA> <NA>
## 65 <NA> <NA> <NA> <NA>
## 66 <NA> <NA> <NA> <NA>
## 67 <NA> <NA> <NA> <NA>
## 68 <NA> <NA> <NA> <NA>
## 69 <NA> <NA> <NA> <NA>
## 70 <NA> <NA> <NA> <NA>
## 71 <NA> <NA> <NA> <NA>
## 72 <NA> <NA> <NA> <NA>
## 73 <NA> <NA> <NA> <NA>
## 74 <NA> <NA> <NA> <NA>
## 75 <NA> <NA> <NA> <NA>
## 76 <NA> <NA> <NA> <NA>
## 77 <NA> <NA> <NA> <NA>
## 78 <NA> <NA> <NA> <NA>
## 79 <NA> <NA> <NA> <NA>
## 80 <NA> <NA> <NA> <NA>
## 81 <NA> <NA> <NA> <NA>
## 82 <NA> <NA> <NA> <NA>
## 83 <NA> <NA> <NA> <NA>
## 84 <NA> <NA> <NA> <NA>
## 85 <NA> <NA> <NA> <NA>
## 86 <NA> <NA> <NA> <NA>
## 87 <NA> <NA> <NA> <NA>
## 88 <NA> <NA> <NA> <NA>
## 89 <NA> <NA> <NA> <NA>
## 90 <NA> <NA> <NA> <NA>
## 91 <NA> <NA> <NA> <NA>
## 92 <NA> <NA> <NA> <NA>
## 93 <NA> <NA> <NA> <NA>
## 94 <NA> <NA> <NA> <NA>
## 95 <NA> <NA> <NA> <NA>
## 96 <NA> <NA> <NA> <NA>
## 97 <NA> <NA> <NA> <NA>
## 98 <NA> <NA> <NA> <NA>
## 99 <NA> <NA> <NA> <NA>
## 100 <NA> <NA> <NA> <NA>
## 101 <NA> <NA> <NA> <NA>
## 102 <NA> <NA> <NA> <NA>
## 103 <NA> <NA> <NA> <NA>
## 104 <NA> <NA> <NA> <NA>
## 105 <NA> <NA> <NA> <NA>
## 106 <NA> <NA> <NA> <NA>
## 107 <NA> <NA> <NA> <NA>
## 108 <NA> <NA> <NA> <NA>
## 109 <NA> <NA> <NA> <NA>
## 110 <NA> <NA> <NA> <NA>
## 111 <NA> <NA> <NA> <NA>
## 112 <NA> <NA> <NA> <NA>
## 113 <NA> <NA> <NA> <NA>
## 114 <NA> <NA> <NA> <NA>
## 115 <NA> <NA> <NA> <NA>
## 116 <NA> <NA> <NA> <NA>
## 117 <NA> <NA> <NA> <NA>
## 118 <NA> <NA> <NA> <NA>
## expensive_ns_stderr
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 2.7525488850331
## 5 3.18362681970739
## 6 2.685004429774
## 7 3.14718649855302
## 8 3.36659651180983
## 9 5.6442382368554
## 10 <NA>
## 11 <NA>
## 12 <NA>
## 13 <NA>
## 14 <NA>
## 15 <NA>
## 16 <NA>
## 17 <NA>
## 18 <NA>
## 19 <NA>
## 20 <NA>
## 21 <NA>
## 22 <NA>
## 23 <NA>
## 24 <NA>
## 25 <NA>
## 26 <NA>
## 27 <NA>
## 28 <NA>
## 29 <NA>
## 30 <NA>
## 31 <NA>
## 32 <NA>
## 33 <NA>
## 34 <NA>
## 35 <NA>
## 36 <NA>
## 37 <NA>
## 38 <NA>
## 39 <NA>
## 40 <NA>
## 41 <NA>
## 42 <NA>
## 43 <NA>
## 44 <NA>
## 45 <NA>
## 46 <NA>
## 47 <NA>
## 48 <NA>
## 49 <NA>
## 50 <NA>
## 51 <NA>
## 52 <NA>
## 53 <NA>
## 54 <NA>
## 55 <NA>
## 56 <NA>
## 57 <NA>
## 58 <NA>
## 59 <NA>
## 60 <NA>
## 61 <NA>
## 62 <NA>
## 63 <NA>
## 64 <NA>
## 65 <NA>
## 66 <NA>
## 67 <NA>
## 68 <NA>
## 69 <NA>
## 70 <NA>
## 71 <NA>
## 72 <NA>
## 73 <NA>
## 74 <NA>
## 75 <NA>
## 76 <NA>
## 77 <NA>
## 78 <NA>
## 79 <NA>
## 80 <NA>
## 81 <NA>
## 82 <NA>
## 83 <NA>
## 84 <NA>
## 85 <NA>
## 86 <NA>
## 87 <NA>
## 88 <NA>
## 89 <NA>
## 90 <NA>
## 91 <NA>
## 92 <NA>
## 93 <NA>
## 94 <NA>
## 95 <NA>
## 96 <NA>
## 97 <NA>
## 98 <NA>
## 99 <NA>
## 100 <NA>
## 101 <NA>
## 102 <NA>
## 103 <NA>
## 104 <NA>
## 105 <NA>
## 106 <NA>
## 107 <NA>
## 108 <NA>
## 109 <NA>
## 110 <NA>
## 111 <NA>
## 112 <NA>
## 113 <NA>
## 114 <NA>
## 115 <NA>
## 116 <NA>
## 117 <NA>
## 118 <NA>write.csv(estimates, paste0("../../local/", env, "_argument_estimated_cost.csv"), quote=FALSE, row.names=FALSE)